Databricks Data Scientist Interview Questions & Guide 2026

Interviewing at Databricks

• Practice bank: 12+ questions
• Rounds: 2
• Typical prep: 1–2 weeks

Interview rounds

Round	Questions
Online Assessment	2
Technical Screen	10

How hard is the Databricks Data Scientist interview?

Difficulty	Share	Questions
Easy	33%	4
Medium	17%	2
Hard	50%	6

What Databricks actually tests for

Topic	Share	Questions
Statistics & Math	42%	5
Data Manipulation (SQL/Python)	33%	4
Machine Learning	17%	2
Coding & Algorithms	8%	1

The 60-second brief

A Data Scientist at Databricks plays a pivotal role in harnessing the power of data to drive insights, create innovative solutions, and enhance the overall user experience across Databricks’ products. This position is crucial, as it directly influences decision-making processes, product development, and strategic initiatives that affect thousands of users and clients worldwide. By leveraging advanced analytics, machine learning, and statistical modeling, you will contribute to projects that tackle complex problems, from optimizing data workflows to developing predictive models that enhance business outcomes.

Databricks candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

What a Data Scientist does at Databricks

As a Data Scientist, you will engage with cutting-edge technologies and methodologies, collaborating with cross-functional teams to ensure that data-driven insights are integrated into product features and organizational strategies. This role is not only technically challenging but also offers the opportunity to influence the direction of products like Databricks' Unified Analytics Platform, which enables organizations to accelerate innovation and improve their data capabilities at scale.

In summary, being a Data Scientist at Databricks means being at the forefront of AI and data technology, where your analytical skills and innovative thinking can significantly impact how data is utilized across various sectors.

What the role owns day to day:

• As a Data Scientist at Databricks, your day-to-day responsibilities will encompass a variety of tasks that drive impactful results. You will be engaged in designing and implementing machine learning models, conducting data analyses, and generating insights that inform product development and strategic initiatives.
• Collaboration is a cornerstone of this role, as you will work closely with engineering, product management, and operations teams to ensure that data-driven insights are integrated into the broader business context. Typical projects may include optimizing data workflows, developing predictive analytics applications, or creating dashboards that visualize key performance metrics.
• Your contributions will not only enhance the functionality of Databricks’ products but also help clients leverage data effectively to achieve their business goals.

What the role is screened against:

• To be a competitive candidate for the Data Scientist position at Databricks, you should possess a blend of technical skills, experience, and interpersonal abilities.
• Proficiency in programming languages such as Python and SQL.
• Strong understanding of machine learning algorithms and statistical methods.
• Experience with data processing tools and libraries, such as Pandas and NumPy.

The Databricks interview process

Databricks candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What it evaluates

• Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
• Whether you name what you have not done instead of stretching to cover every line of the posting
• Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

• Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
• Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
• Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

Technical Assessments

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What it evaluates

• Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
• Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
• Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

• Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
• Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
• If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows

Behavioral Discussions

This round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.

What it evaluates

• Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
• The counterfactual: what would have been done without your analysis, and why that default was worse
• How far your involvement ran past the handoff, and whether you checked that the change did what you predicted

How to prepare

• Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
• Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
• Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.

Case Studies

Underneath the business framing, this round is usually asking whether you can turn a fuzzy goal into a quantity that could be computed from data such a business would plausibly hold. That means a metric with a stated numerator, denominator, eligibility rule and time window, plus an honest account of the conditions under which it would mislead you. Answers come apart when a candidate names a familiar metric and never defines it, because every follow-up then lands on an ambiguity that was left open and the candidate has to invent the definition under pressure.

What it evaluates

• Whether a named metric arrives with its denominator, eligibility rule and window attached rather than assumed
• Whether the measure follows from the mechanism you proposed, or is a recognisable metric retrofitted to it afterwards
• Whether you name a guardrail that would reveal the gain came from somewhere you did not want it to come from
• Whether you can say what data the plan requires and what you would settle for if that logging were never implemented

How to prepare

• Take five metrics you reach for by reflex and write each as one sentence containing numerator, denominator, eligibility rule and time window. The ones you cannot finish are the ones that will fail under follow-up.
• For a product you use daily, write the measurement plan you would propose for a change to it: primary metric, one guardrail, the unit of analysis, and the table the numbers would come from.
• Practise the substitution question. For three metrics you like, write what you would measure instead if the event you depend on were not being logged.

Final Interviews

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What it evaluates

• Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
• Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
• Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
• Whether you ask what decision is actually on the table before explaining anything

How to prepare

• Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
• For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
• Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else

Questions Databricks candidates report

Product sense and metrics

When the prompt says a number moved, the first question is whether it moved. Instrumentation changes, a redefinition, a backfill, a release that drops events, bot traffic and a plain holiday all produce the same shape as a real behaviour change. Confirm the move survives those before you reason about users at all. After that, localise instead of speculating: cut by platform, geography, tenure and acquisition source, and ask whether the aggregate fell because a segment changed or because the mix of segments changed. Weak answers list ten plausible causes. Strong ones order the cuts so each result eliminates several at once.

• How would you approach building a recommendation system for a retail client?
• How do you prioritize competing tasks and deadlines?
• Describe a scenario where you had to make a decision based on incomplete data.
• Given a dataset on customer behavior, what steps would you take to derive actionable insights?

SQL and data manipulation

Funnels and retention read as counting problems and behave as time problems. Step two has to follow step one for the same user, inside a window someone has to define, and a cohort clock usually starts at each user's own first event rather than at a calendar boundary. Before writing, pin down which timestamp is event time rather than load time, whether your interval is half-open so a midnight row lands in exactly one bucket, and what time zone the reporting day belongs to. The usual failure is a query that looks right and whose day boundaries quietly belong to a different zone than the number it is compared against.

• Discuss the trade-offs between different database technologies for data storage.
• Provide an example of how you would optimize a slow-running query.

A/B testing and experimentation

Two hard parts of this work share a root, which is being honest about what a procedure actually guarantees. Watching a dashboard and stopping when it turns green is one, because a fixed-horizon test controls its false positive rate only at the horizon you declared, so every extra look is another chance to cross the line; the fix is a method built for continuous monitoring, such as group sequential boundaries or always-valid confidence sequences. The other is what to do when randomisation is not available, where the work is naming the assumption your quasi-experiment buys and showing how you would test it.

• Explain how you would design an A/B test for a new feature.

Statistics and probability

Most of these questions end, explicitly or not, with a stakeholder who wants a decision and does not want a lecture, which makes precision of language the thing actually under test. A p-value is the probability of data at least this extreme if the null were true. It is not the probability that the null is true, and not the chance the result is a fluke. A 95% interval is a property of the procedure across repeated samples, not a 95% chance that this particular interval contains the parameter. Give the plain sentence that is still true, then the caveat that would change the decision.

• Write a function to calculate the mean and variance of a dataset.

Machine learning and modelling

Before any algorithm, write the label as a query you could run today: which event counts, over which window, for which rows, and what happens to rows whose window has not closed yet. Then say what decision the score feeds and what action follows once it crosses a threshold, because that fixes both the metric and the cost of being wrong in each direction. Weak answers open with a model family. Strong ones pin down the label and the decision first, then let the modelling choices fall out of them.

• What factors do you consider when scaling a machine learning model in production?

Behavioural and stakeholder questions

A large share of these land on disagreement: someone wanted a different answer, a different metric, or a different date. The weak version of the answer makes the other person wrong and the data right. The strong version treats their position as a constraint you had not priced in, usually a cost, a deadline, or a commitment made outside your view. Before you say what changed their mind, say what evidence would have changed yours. That one sentence separates someone who argues with numbers from someone who reasons with them, and it is the sentence that survives the follow-up.

• What motivates you to work in data science?

Other topics

A few of these have no retrievable answer at all. They ask what you would prioritise with one analyst and two weeks, whether a model is good enough to ship, whether a data source is appropriate to use. Answer by naming the decision, then the cost of being wrong in each direction. A false positive that sends an email and a false positive that suspends an account do not deserve the same threshold, and any argument about the threshold only makes sense once those costs are on the table. Say what you would do, then say what evidence would change it.

• Explain how you would ensure data quality and integrity in a large-scale system.

Practice exercises with worked solutions

The prompts above tell you what gets asked.

Consecutive qualifying weeks before renewal, as a ranked worklist

fct_api_request carries account_id, workspace_id, environment, request_at (timestamptz), http_status and traffic_class. fct_subscription_period carries account_id, term_end_date, auto_renew and is_current. A qualifying week for an account is an ISO week with at least 50 successful production requests in traffic_class ('interactive','batch'). Over the last 52 whole ISO weeks, and for accounts whose current term ends within 90 days, return the current qualifying-week streak length, the week it began, and the longest earlier streak. An account whose streak has broken must appear with a current length of zero.

How a strong candidate works it

• Bucket weeks as date_trunc('week', request_at AT TIME ZONE 'UTC'). request_at is a timestamptz, so an unpinned date_trunc silently uses the session time zone, weeks start at a local midnight, and Monday-morning traffic lands in the previous week for part of the fleet. Pinning UTC also makes the seven-day arithmetic below exact across daylight-saving transitions.
• Apply the exclusions before counting: environment = 'production', http_status < 400, traffic_class IN ('interactive','batch'). Then apply the volume floor and drop the current partial week, which can never meet a floor calibrated on whole weeks.
• Build islands with the row-number anchor: ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY week_start) as rn, then week_start - rn * interval '7 days' is constant inside a run of consecutive weeks. Group by that anchor to get each streak's start, end and length.
• The current streak is the island whose end equals the last whole week; if none does, the account's current streak is zero and that is the interesting case. The longest earlier streak is the maximum length among the remaining islands.
• Join the renewal filter from the current subscription row and LEFT JOIN the streak summary so an account with no qualifying week at all still appears, rather than vanishing from the risk list precisely because it went quiet.
• Finish with an operating point. The list is worked by a team with finite capacity, so order it and cut it at that capacity, and say what happens to the accounts below the line.

Expected result (40 min): One row per renewal-eligible account with current_streak_weeks, current_streak_start_week and longest_prior_streak_weeks, where an account with no qualifying week in the last whole week returns current_streak_weeks = 0 and still appears, ordered so the quietest imminent renewals sit at the top.

Check yourself

• Per account, the sum of all island lengths equals the count of qualifying weeks for that account.
• current_streak_weeks > 0 if and only if the last whole week is a qualifying week for that account.
• No island contains a gap: re-deriving consecutive differences inside an island returns exactly 7 days everywhere.
• An account qualifying in all 52 weeks returns a single island of length 52 and a null-free current streak start.

Follow-ups

• The floor of 50 requests was picked for this exercise. How would you calibrate it from data, and what would force a recalibration?
• A regional holiday week drops several accounts below the floor at once. How do you keep that out of the risk list?
• How would you evaluate whether contacting these accounts actually changed renewal, given that coverage is assigned deliberately?

Join daily usage to the contract version live that day

fct_usage_daily has account_id, usage_date and net_amount_cents. fct_subscription_period has account_id, subscription_period_id, plan_tier, term_start_date, term_end_date, arr_cents and is_current, with one row per contract term version and every amendment inserting a new row. Attach to each usage row the subscription_period_id whose term brackets usage_date (term_start_date <= usage_date <= term_end_date). pd.merge_asof and an interval-condition merge are unavailable; use sorting and numpy.searchsorted. Then report monthly net revenue by plan_tier. Terms for one account do not overlap, and usage may fall outside every term.

How a strong candidate works it

• Say out loud why the cheap version is wrong: joining on is_current stamps today's plan tier onto last year's usage, so every account that upgraded has its history reclassified and revenue-by-tier becomes a function of when the query ran.
• Sort each account's terms by term_start_date and use np.searchsorted(term_start, usage_date, side='right') - 1 to get the last term that started on or before the usage date. Do it per account group, or globally after encoding (account_id, date) into one monotone key.
• searchsorted only enforces the left edge. Validate the right edge afterwards — usage_date <= the candidate's term_end_date — and set the match to NA where it fails. That NA is usage in a gap between contracts and must stay visible instead of being folded into the expired term.
• Assert non-overlap before trusting the lookup, and write the assertion so it is capable of passing. prev_end = terms.groupby('account_id').term_end_date.shift() is NaT on each account's first row, and NaT < Timestamp evaluates to False rather than NA, so a comparison followed by .fillna(True) has nothing left to fill and the assertion fires on every account's first term whatever the data looks like. Guard the null yourself: assert (prev_end.isna() | (prev_end < terms.term_start_date)).all(). The failure mode of getting this wrong is not a false alarm you notice once — it is an assertion someone deletes because it never passes, after which overlapping terms make searchsorted return one of them with no trace in the output.
• Aggregate after the join, grouping by (usage_date month, plan_tier) with dropna=False so the unmatched bucket appears as its own row and the total still ties to the ungrouped sum of net_amount_cents.

Expected result (35 min): Exactly one output row per input usage row, a subscription_period_id that is NA precisely for usage outside every term, and a monthly table whose values sum to the ungrouped total of net_amount_cents once the NA-tier bucket is included.

Check yourself

• len(joined) == len(usage); the join must not fan out.
• joined.net_amount_cents.sum() == usage.net_amount_cents.sum().
• For every matched row, assert term_start_date <= usage_date <= term_end_date directly rather than trusting searchsorted.
• Run the non-overlap assertion on a frame where every account has exactly one term: prev_end is NaT throughout, so the assertion must pass. An assertion that fires there is testing the shift, not the data.
• Spot-check one account that upgraded mid-term: its usage before the amendment must still carry the old plan_tier.

Follow-ups

• An amendment takes effect on the 17th of a month. How do you report that month's revenue by tier?
• What changes if terms can overlap because of a co-term amendment?
• How would you verify this against a SQL implementation using a BETWEEN condition?

Stop metering retried requests: design the metric that decides it

The platform meters accepted requests. A proposal carries three clauses: stop metering fct_api_request rows where is_retry = true, stop metering rows with a 4xx status, and stop metering rows with http_status >= 500. Its author has attached one figure to all three together, roughly 4% of requests_thousands volume. You have fct_api_request (account_id, is_retry, idempotency_key, http_status, traffic_class, billable_units, request_at) and fct_usage_daily (billable_quantity, net_amount_cents, cogs_cents). Size each clause separately before arguing about any of them, then define the primary metric, the guardrail that genuinely conflicts with it, and how you resolve that conflict for a decision that has to be made this quarter. Revenue falls this quarter with certainty; any benefit appears at renewals up to twelve months out.

How a strong candidate works it

• Size the three clauses before accepting the headline 4%, because one of them is a no-op. billable_units is defined as zero for requests that failed with a 5xx, so the third clause removes no metered volume at all. Confirm that in the data rather than trusting the column comment: if sum(billable_units) over rows with http_status >= 500 is not zero, the metering pipeline contradicts its own definition and that is a billing defect to file before any pricing conversation happens. The two live clauses are 4xx failures, which are metered in full, and retries that did not themselves end in a 5xx.
• Size the two live clauses as a union, not a sum. A retry can return 4xx, so the clauses overlap and adding their volumes counts that intersection twice. Partition the trailing 90 days into four mutually exclusive buckets instead: clean (is_retry = false, http_status < 400), non-retry 4xx, retry with http_status < 500, and http_status >= 500. Report the removable share per account as a distribution; if the mass sits in a handful of accounts this is a commercial conversation with those accounts rather than a platform-wide pricing change.
• State the conflict rather than dissolving it. The primary metric, net metered revenue per paying account, and the integrity guardrail, the share of metered volume that is retried or failed traffic, move in opposite directions by construction. No redefinition removes that. The job is to price the trade-off, not to make it disappear.
• Show the perverse coupling with data, and be exact about its mechanism. Because a 5xx already carries zero billable_units, the platform is not paid directly for its own failures; it is paid for the retries and the client-side 4xx traffic those failures provoke, which is one step removed and therefore easy to miss. Cross-tab each account's trailing 28-day 5xx rate against its metered volume in the same window. If metered volume rises with error rate, that indirect coupling is the actual argument for the change.
• Resolve on expected value with the uncertainty stated. The revenue loss is computable and near-certain; the renewal benefit is not, so invert it and state the break-even: how many basis points of gross logo retention on the renewal-eligible base would offset the loss. That converts an argument about values into an argument about one number. Then propose the measurement that would settle it instead of claiming a readout you do not have: stage the rollout by renewal cohort so accounts whose terms end soonest are treated first, read out on gross logo retention on the renewal-eligible base, and say honestly whether the number of annual renewals in the window can support that estimate at all.

Expected result (30 min): Three clause sizes rather than one blended figure: zero for the 5xx clause, since billable_units is already zero there, and separate volumes for 4xx failures and for retries that did not 5xx, combined as a union so the retry-and-4xx intersection is counted once. On top of that, a single break-even figure stated as the improvement in gross logo retention on the renewal-eligible base needed to offset the metered revenue actually removed, and the per-account concentration of that removable volume. The answer says that the primary metric and the integrity guardrail conflict by construction, and resolves it with the break-even number and a staged rollout rather than by moving a definition.

Check yourself

• sum(billable_units) over rows with http_status >= 500 must be exactly zero; any other result contradicts the documented definition of the column and invalidates the volume figures derived from it.
• The four buckets must be mutually exclusive and must sum to total billable_units, which must in turn reconcile to fct_usage_daily billable_quantity within the known restatement tolerance; a large gap means the request-to-SKU mapping is wrong.
• The two removable buckets must be reported as a union; their arithmetic sum will exceed the union by exactly the retry-and-4xx intersection, and a write-up that quotes the sum has overstated the revenue at risk.
• Requests sent with no idempotency_key cannot carry is_retry = true, so the measured retry share is a lower bound and the write-up must say so.

Follow-ups

• Suppose the two live clauses turn out to remove 2.6% of consumption revenue. How much improvement in gross logo retention on an annual-contract base pays that back, and over what horizon does the payback land?
• A retry sent without an idempotency_key cannot be flagged as a retry. Which direction does that bias your estimate of the removable volume, and how can you bound it?

Net revenue retention jumps sixteen points in one month

Trailing-twelve-month net revenue retention printed around 108 percent for months and now reads 124 percent, with no unusual deals closed. The query sums arr_cents from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, superseded_by_id, is_current, booked_at) filtered on is_current = true at month M, across accounts holding arr_cents > 0 at month M-12. Find the defect, correct the number, and rewrite the definition so the next person cannot reintroduce it.

How a strong candidate works it

• Audit the grain before the arithmetic: count account_ids holding more than one row with is_current = true and superseded_by_id null. A versioned contract table that double counts one amendment batch inflates the numerator while leaving the denominator untouched.
• Replace is_current with an as-of selection on both dates, taking the version whose term_start_date and term_end_date bracket the reporting date and tie-breaking on latest booked_at. The numerator is read as of M and the denominator as of M-12; neither uses today's live version.
• Verify the cohort is frozen. The account set is fixed at M-12 and nothing acquired since may enter the numerator, so check that no join to a current-period table quietly re-admits new accounts.
• Confirm the estimand is a ratio of sums rather than a mean of per-account ratios. Contraction is floored at zero while expansion is unbounded, so the two constructions differ systematically and the second is far noisier.
• Reissue the definition with the failure modes written into it: exactly one row per account per date by construction, cohort frozen at M-12, churned accounts contributing zero rather than dropping out of the numerator.

Follow-ups

• A churned account should contribute zero rather than disappear. What does the ratio do under each treatment, and which one is correct?
• How would you unit-test this metric so a future amendment batch with the same defect fails a check instead of reaching a board slide?

Walk through an analysis you later discovered was wrong

Six weeks ago you reported that consumption fell 9 percent in the last week of the month, and a team spent a sprint investigating the cause. The fall was an artefact: rows in fct_usage_daily land late and are restated in place, and you queried before the tail had settled. Describe how you found the error, what you told the people who acted on it, and the control you put in place so this class of mistake cannot reach a dashboard again. Be specific about how the settling window was measured.

How a strong candidate works it

• The interviewer is probing whether you self-report errors before someone else finds them, and whether your fix is structural rather than a promise to be more careful. Say plainly that the number was wrong and that a sprint was spent on it, before describing any diagnosis.
• Establish the artefact quantitatively instead of asserting that data lands late. For each usage_date, compare the total as of first_written_at against the settled total and read the settling time off that curve, for example 97 percent of final by day three and 99.5 percent by day five.
• Correct the record the same day, in the channel the original number went out in, to the same audience. The cost of the wasted sprint belongs in the correction, not in a footnote.
• Make the fix structural: exclude a trailing lag window from every reportable figure, and make the reporting view return no rows inside that window rather than returning partial ones. A dashboard that shades unsettled days still gets read as a decline.
• State what generalises. Any fact table restated in place has this failure mode, so the guard belongs at the source rather than on the one dashboard that embarrassed you. A strong answer ends with the class of error closed; a generic one ends with a lesson learned.

Follow-ups

• How did you choose the completeness threshold behind the lag window, and what would make you recalibrate it?
• What did you say to the team that lost the sprint, and what did they say back?
• Is there a legitimate case for showing the unsettled tail at all, and to whom?

Scope an open-ended request to predict account churn

A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.

How a strong candidate works it

• The interviewer is probing whether you convert a vague request into a decision with a capacity constraint attached. A candidate who starts talking about model families has already failed the exercise.
• Pin the event and the horizon first. Churn is only possible at term_end_date, so the population is accounts renewing in the next 60 to 90 days, not the whole base. Ask explicitly whether contraction and downgrade count as churn or only full non-renewal, because the three have different base rates and different interventions.
• Pin the action and the capacity. Six people times a realistic number of meaningful interventions per week gives k, and k is what the list is ranked to. Evaluate on precision at k rather than a global AUC over accounts that will never be contacted.
• Audit leakage before choosing features. Every feature needs a timestamp proving it existed before the prediction date. A downgrade amendment, a churn reason code, and a ticket opened after the renewal conversation started are all leaks that will make the offline number look excellent and the live list useless.
• Ask for the counterfactual now rather than later. Coverage is assigned deliberately, so without a held-out slice agreed at the start the intervention can never be evaluated, and you will be asked for its impact in nine months regardless.
• Propose the smallest artefact that closes the loop: a weekly ranked list sized to capacity with two or three inspectable reasons per row, plus a stated policy for accounts below the line.

Follow-ups

• The director insists all accounts are in scope, not only those renewing soon. How do you answer without simply refusing?
• Historical non-renewals number about 30 a year. At what point do you tell them a model is the wrong tool and a rules list is better?
• Which candidate features would you drop purely because you cannot date them?

Where candidates lose points

Comparing accounts that received a sales or customer-success touch against those that did not

Assignment of coverage is deliberate and pulls in both directions at once: the largest accounts get a named owner because they are valuable, and the accounts showing distress get one because they are at risk. The comparison therefore mixes a strong positive selection with a strong negative one, and the naive estimate can come out with either sign depending on which assignment rule dominated during the period examined. Nothing about matching on observed size fixes this, because the risk signal that triggered coverage is usually the same signal that predicts the outcome. It needs either an actual randomised or staggered rollout of coverage, or a design built on a capacity constraint or territory boundary that assigns coverage for reasons unrelated to account health.

Reading consumption metrics before the metering lag window has closed

Usage pipelines land late and correct themselves, which is exactly what is_restated and restated_at record. A dashboard queried on day T sees a partially populated tail for the last several days, so the most recent points always slope downward and always look like a regression. Analysts then explain the artefact, and sometimes ship a change to fix it. Establish the empirical settling time by measuring how much a given usage_date's total moves between first_written_at and its final value, exclude that many trailing days from every reportable figure, and never compare a fresh period against a settled one.

Averaging per-user rates to produce a population rate

Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

Before the Databricks loop

• Preparation is key to succeeding in your Data Scientist interview at Databricks. You should focus on demonstrating both your technical expertise and your problem-solving capabilities, as these will be critical in evaluating your fit for the role.
• Role-related Knowledge – This criterion assesses your understanding of data science concepts, tools, and methodologies. Interviewers will expect you to be proficient in areas such as machine learning algorithms, statistical analysis, and programming languages like Python and SQL. You can demonstrate strength by discussing relevant projects and the impact of your work.
• Problem-Solving Ability – Your approach to tackling complex problems will be under scrutiny. Interviewers will evaluate how you structure your thought process, your creativity in finding solutions, and your ability to draw meaningful insights from data. Be prepared to walk through your problem-solving strategies and articulate your reasoning.
• Leadership – While this role is primarily technical, showcasing your ability to lead projects and influence teams is important. You should communicate how you collaborate with others, manage project timelines, and navigate challenges within a team setting. Examples of past experiences where you took initiative will be valuable.

Breadth then depth, for a generalist facing a mixed loop

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

• Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
• For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
• Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

• Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
• Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
• Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

• Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
• Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
• Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

• Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
• For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
• Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

• Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
• Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
• Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

• Repeat the depth protocol on the second-ranked area with the same six-problem structure.
• Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
• Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

• Re-run the six prompts from day one under the same clock and compare both correctness and time.
• Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
• Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.