Interview guides · Skyworks Solutions

Skyworks Solutions Data Scientist Interview Questions & Guide 2026

Skyworks Solutions data scientist prep: probability, sampling distributions, and stating the assumption behind every test you run.

The 60-second brief

A Data Scientist at Skyworks Solutions plays a pivotal role in shaping the future of wireless connectivity. As a global leader in high-performance analog semiconductors, the company relies heavily on data-driven decision-making to optimize manufacturing yields, streamline global supply chains, and accelerate hardware design. In this role, you will bridge the gap between complex physical engineering and advanced predictive modeling, turning massive datasets into actionable strategic advantages.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

Focus areas

Correct late-entered timesheets before trending recent weeks
Segment margin by pricing model before comparing
Compute utilisation against a defended availability denominator

Overview

Your work will directly impact highly sophisticated products that power everything from the latest smartphones to automotive systems and aerospace technology. Unlike pure software environments, data science at Skyworks Solutions operates at the intersection of hardware, manufacturing, and machine learning. You will work on optimizing wafer fabrication processes, predicting equipment failures before they occur, and modeling radio frequency (RF) component performance to reduce time-to-market.

This position offers an intellectually challenging environment where physical-world constraints meet digital-world scale. Successful candidates are those who possess not only deep statistical and machine learning expertise but also a strong curiosity about how physical products are designed, manufactured, and shipped globally.

The Skyworks Solutions interview process

Skyworks Solutions candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Phone Pre-Screening

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Technical Rounds

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

HR/Behavioral Round

Most of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.

What to demonstrate

Whether you can state the other side's argument accurately before you explain why you disagreed
What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
Whether you distinguish being overruled from being wrong, and can give an example of each

How to prepare

Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.

Onsite/Virtual Loop

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 to demonstrate

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

Where candidates lose points

Using calendar hours, or a current rate card, as the denominator

Available hours is not days x 8. It must subtract approved leave and region-specific public holidays, scale by fte_fraction, and prorate for hire_date and termination_date inside the period; skipping this produces cross-region utilisation gaps that are purely holiday-calendar artifacts and a fake dip every time a cohort of new hires starts. Equally, bill and cost rates change over time, so joining today's dim_consultant row to last year's hours restates history. Use the rate snapshotted on the time entry, or join dim_consultant on work_date BETWEEN effective_from AND COALESCE(effective_to, 'infinity').

Trending utilisation or revenue on work_date without accounting for timesheet backfill

Time entries are created days to weeks after the work happens, and the backfill tail often runs two to six weeks. A dashboard keyed on work_date therefore shows the most recent weeks as a decline that reverses on every refresh. The fix is either to hold the reporting window back past the observed backfill tail (measure the tail with the timesheet submission lag metric rather than guessing) or to report an as-of-entered_at snapshot so the series is internally consistent, and to state which one you used.

Extrapolating a first-week lift inflated by novelty effects

Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.

Generalising beyond the population the sample actually supports

State the frame the sample was drawn from and where it diverges from the population you want to talk about: time window, platform, geography, opt-in. If a group is excluded from the frame, either weight to known margins or narrow the claim rather than quietly extending it.

Questions & practice

How do you manage data versioning and model registry when deploying machine learning models to production?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Pick an evaluation metric that matches the cost of each error type, not a default.
Say how the offline result would be validated online before it is trusted.

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

How do you prevent overfitting when training a complex model on a relatively small set of experimental laboratory data?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Check what information would not exist at prediction time, and exclude it.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

How would you choose the decision threshold, and who owns that choice?
Where could label leakage enter this setup?

Attach effective-dated cost rates without merge_asof

Medium · type-2 scd · interval join · searchsorted

time_entries (consultant_id, work_date, hours) has roughly two million rows. dim_consultant is a type-2 dimension (consultant_sk, consultant_id, standard_cost_rate_usd, effective_from, effective_to nullable, is_current) with a handful of rows per consultant. Attach the cost rate in force on each entry's work_date. You may not use pandas.merge_asof and you may not materialise the consultant_id cartesian product. Treat intervals as half-open, meaning [effective_from, effective_to), with a NULL effective_to open-ended. Return unmatched entries separately with a reason rather than dropping them.

Approach

Fix the semantics before writing code: half-open intervals are what make the join single-valued on the day a rate changes. Compare work_date at start-of-day in the reporting timezone against effective_from in the same zone, and write that comparison down as a stated assumption.
Sort dim_consultant by (consultant_id, effective_from) and assert non-overlap inside each consultant, since searchsorted only returns the right answer if the intervals are ordered and disjoint.
Build the composite sort key so it cannot overflow: dense-code the consultant ids through one shared sorted category mapping, express time as whole days since a floor date earlier than every date in both frames, and take SCALE as a power of two above the largest day offset, giving key = code * SCALE + offset. Assert code.max() * SCALE + off.max() < np.iinfo(np.int64).max before using it. Multiplying a raw consultant_id by a scale larger than a nanosecond timestamp (about 1.75e18 for present-day dates) leaves an id budget of only int64_max / SCALE, roughly five consultants, before the product passes 9.22e18; beyond that numpy wraps silently, and it does not even wrap consistently negative (with SCALE = 2e18 an id of 12 lands back on a large positive key), so the order breaks without a sign flip to catch it.
With a key that fits, np.searchsorted(dim_key, entry_key, side='right') - 1 is the index of the last interval starting at or before work_date, in O((n + k) log k) and with no cartesian product. SCALE only has to exceed the span of the offsets, never an absolute epoch value, so if a rate can change intraday, switch the offset unit to seconds (a 2^31 scale covers 68 years) and re-run the same assertion.
Validate the candidate rather than trust it: the match holds only if work_date < effective_to or effective_to is NULL. Everything else is an exception, either work logged before the first dimension row or a genuine gap in the SCD.
Return matched rows carrying consultant_sk and the rate, plus an exceptions frame with counts by reason, so a gap in the dimension surfaces as a number rather than as quietly missing cost.

Worked solution (30 min)

dim = dim.sort_values(['consultant_id','effective_from']); assert within each consultant that effective_from is strictly increasing and effective_to.fillna(max) >= the next effective_from.
cats = np.union1d(time_entries.consultant_id.unique(), dim.consultant_id.unique()), which is sorted, so code = pd.Categorical(ids, categories=cats).codes.astype('int64') preserves consultant_id order and the sort above still holds; an id absent from the dimension codes to -1 instead of matching something else.
floor = min(work_date.min(), effective_from.min()).normalize(); off = (ts.dt.normalize() - floor).dt.days.to_numpy() in both frames; SCALE = 1 << 20 (1,048,576 days, about 2871 years); assert off.min() >= 0, off.max() < SCALE and code.max() * SCALE + off.max() < np.iinfo(np.int64).max; key = code * SCALE + off.
idx = np.searchsorted(dim_key, entry_key, side='right') - 1; entries whose code is -1 are exceptions with reason 'consultant_not_in_dimension'; rows with idx < 0, or whose matched dimension code differs from the entry's code, are exceptions with reason 'no_interval_before_work_date'.
Check the upper bound: keep rows where effective_to is NaT or work_date < effective_to; the rest are exceptions with reason 'gap_in_scd'.
Brute-force 200 random entries with a straight boolean filter over that consultant's rows and diff the rates against the vectorised answer.

Expected result: Matched rows equal to the input count minus exceptions, each carrying exactly one consultant_sk and standard_cost_rate_usd, plus an exceptions frame grouped by reason. No input row is duplicated and none is silently dropped.

Verify it yourself

len(matched) + len(exceptions) == len(time_entries), which is the property an inner join would break.
The 200-row brute-force sample agrees on every rate.
An entry whose work_date equals an effective_from takes the new rate, not the old one, exactly once.
Per-consultant total cost changes by (new_rate - old_rate) * hours after the change date, and by nothing before it.
np.diff(dim_key) is strictly positive, asserted rather than assumed: this is the only cheap guard against a key that has wrapped, since an overflowed product can land back on a positive value and a non-negativity check alone would pass it. Renumbering the consultant_ids (say to 4000001..4000050) must not change a single attached rate, which is the fixture a raw-id-times-nanoseconds key fails once ids run past int64_max / SCALE.

Follow-up

Rates were backdated a quarter after the fact. How do you keep last quarter's published cost from moving when someone reruns the report?
How does the implementation change if effective_to is inclusive rather than exclusive, and what breaks on the boundary day if you get that wrong?

Explain how you would implement cross-validation from scratch in Python without using scikit-learn.

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.

Follow-up

How would you verify this result without re-running the same query?
How does the query change if the join becomes one-to-many?

Write a Python function to clean and merge two large, unstructured datasets containing sensor timestamps and chip test results.

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Say which table is the grain you start from, and join outward from it.

Follow-up

How does the query change if the join becomes one-to-many?
How would you verify this result without re-running the same query?

How would you optimize a SQL query that joins multiple multi-million-row tables tracking wafer fabrication steps?

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.

Follow-up

How does the query change if the join becomes one-to-many?
How would you verify this result without re-running the same query?

Longest consecutive bench spell per consultant from weekly activity

Hard · gaps and islands · window functions · run length

You have a dense series (consultant_id, week_start, billable_hours, full_week_leave BOOLEAN) covering 26 weeks for every billable-role consultant, with zero-hour weeks present as 0. Return every maximal run of consecutive weeks where billable_hours = 0, as consultant_id, spell_start_week, spell_end_week and week_count, then each consultant's longest run. Weeks flagged full_week_leave must neither break a run nor count toward its length. Mark runs still open at the last week of the series rather than reporting them as finished.

Approach

Remove full_week_leave weeks from the sequence first, then re-number what remains with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_start). Deleting before numbering is precisely what makes a leave week transparent to a run instead of a break in it; filtering afterwards would leave a gap in the sequence and split the spell.
Flag each surviving week as bench (billable_hours = 0) or active, then compute two row numbers per consultant: one over all surviving weeks, one partitioned additionally by the bench flag. Their difference is constant inside any run of adjacent same-flag weeks, which is the island key.
Group the bench weeks by (consultant_id, island key) and take MIN(week_start), MAX(week_start) and COUNT(*). Do not island on week_start minus an interval times a row number: that variant assumes the spine has no holes, and the leave removal has just deliberately put holes in it.
Pick the longest spell per consultant with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_count DESC, spell_start_week) so ties resolve deterministically, rather than a MAX that cannot carry the accompanying dates.
Handle both edges as censoring. A spell touching the last week of the series is still open and its length is a lower bound, so flag it; a spell touching the first week may have started earlier, so either flag it too or exclude it from any length distribution and say which.

Worked solution (40 min)

Filter out full_week_leave rows and count the surviving weeks per consultant.
Add the bench flag and the two row numbers, then print one consultant's full sequence to see the island key stay constant across a run.
Group bench weeks by consultant and island key and aggregate to spell boundaries and lengths.
Add the open-at-the-edge flag by comparing spell_end_week to the consultant's last surviving week.
Rank spells per consultant and select the longest with the deterministic tiebreak.

Expected result: One row per maximal bench run with its first and last week, its length in weeks and an is_open flag, plus a one-row-per-consultant longest-spell table. A consultant with no bench weeks appears in neither, which is correct and worth stating.

Verify it yourself

For each consultant, the sum of week_count across all spells equals their count of bench weeks in the leave-filtered series.
No two spells for the same consultant are adjacent in the re-numbered sequence; adjacency would mean the island key was computed wrongly.
The longest-spell table has at most one row per consultant and its week_count matches the maximum in the spells table.
Hand-check one consultant by printing their 26 weeks as a strip of zeros, hours and leave markers, and reading the runs off it.

Follow-up

A spell begins before your 26-week window. How do you detect that, and what do you report as its length?
Rebuild this at daily grain. What changes about weekends, holidays and the island key?
How would you separate true bench from unbilled delivery work on a live engagement, given only charge codes?

How do you prioritize your work when multiple engineering teams are requesting data science support simultaneously?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

Given a stream of real-time testing data, how would you design a data pipeline to ingest, process, and store these metrics with minimal latency?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

If you are handed a dataset with 40% missing values in key engineering metrics, how would you decide whether to impute, ignore, or drop those columns?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

Which segment would you cut first, and what would that rule out?
How would you detect that the metric is being gamed rather than genuinely improving?

We want to implement predictive maintenance on a critical piece of manufacturing equipment. What data would you collect, and how would you structure the target variable?

Medium

Approach

Clarify what is being asked and what a complete answer would contain.
Say what you would check first and why it is the highest-information step.
Work from the decision backwards to the evidence you would need.

Follow-up

How would you know your answer was wrong?
What assumption would you test first?

Cut variance with pre-period margin before buying more accounts

Medium · cuped · variance reduction · stratification · ancova

You are designing an account-randomised pilot of a delivery-review process, measured on engagement gross margin over one quarter. The design is underpowered at the account count available. Each account has two prior quarters of margin history reconstructible from fct_invoice_line and fct_time_entry, correlated 0.6 with the pilot-quarter outcome. Specify a variance-reduction plan: the estimator, the coefficient it uses, how you handle accounts whose first engagement starts inside the pilot window, and the sample-size equivalent of the reduction you expect. State the precondition that makes the adjustment safe.

Approach

Define the adjusted outcome Y* = Y - theta * (X - Xbar) with X the pre-period margin and theta = Cov(Y, X) / Var(X). The precondition is that X is measured strictly before assignment, so its arm means agree in expectation and subtracting it removes the account-level variance component without touching the treatment contrast.
State the payoff exactly: Var(Y*) = Var(Y) * (1 - rho^2). At rho = 0.6 that is a 36% variance reduction, a 20% standard-error reduction since sqrt(0.64) = 0.8, and a sample-size equivalent of 1 / (1 - rho^2) = 1.56 times the accounts. Real, but it will not rescue a design that is five times short.
Implement it as the ANCOVA regression of Y on treatment and X, which is numerically the same estimator when theta is the OLS coefficient and which hands you cluster-robust inference for free. Estimate theta on pooled data, never separately per arm, which reintroduces bias.
Keep accounts with no history rather than dropping them: impute X at the sample mean and add a no-history indicator. Because X is pre-treatment, imputation cannot bias the treatment coefficient, while dropping those accounts silently changes the estimand to accounts with history.
Balance at assignment as well as adjusting after it. Block on account_tier and pre-period margin tercile, randomise within block, and carry block fixed effects into the model. At roughly 120 accounts, blocking guarantees balance where post-hoc adjustment only achieves it on average.

Worked solution (25 min)

Build the account-quarter margin panel: fees from fct_invoice_line where line_type in (fees, milestone, credit_note), cost as approved fct_time_entry hours times cost_rate_usd, rolled up through parent_client_id.
Estimate rho between the pre-period window and a historical quarter that stands in for the pilot, and confirm it is near the assumed 0.6 rather than assuming it.
Fit Y on treatment, X and block fixed effects with standard errors clustered at client_id; read theta off the X coefficient.
Compare the standard error on the treatment coefficient with and without X in the model.
Recompute the MDE from the reduced standard error and test it against the decision threshold before committing to the pilot.

Expected result: At rho = 0.6 the adjusted standard error should come in about 20% below the unadjusted one, equivalent to 1.56 times the accounts, and the two treatment-effect point estimates should agree within about one standard error since both are unbiased.

Verify it yourself

The treatment coefficient with and without the covariate must be close; a large shift means X is imbalanced across arms, which is either a randomisation failure or a covariate that is not actually pre-treatment.
Realised variance reduction measured on the pilot data should be near 1 - rho^2; a much smaller reduction means rho was estimated on a period that does not resemble the pilot.
Dropping the no-history accounts entirely should move the point estimate only within noise; a large move means those accounts differ systematically and the estimand has changed.

Follow-up

Why is pre-period outcome usually a better covariate than account_tier alone, and when does that ranking flip?
A colleague wants to add hours worked during the pilot quarter as a second covariate. What happens to the estimate?
Your pre-period window is two quarters. What sets the optimal length, and what degrades if you extend it to eight?

Win rate jumped after a pipeline hygiene push

Medium · denominator effects · selection · pipeline

Competitive win rate by value jumped from 38% to 51% in one quarter. The metric sums expected_value_usd from fct_proposal where stage = 'won' over the same sum where stage IN ('won','lost'), filtered to is_competitive = TRUE and decided_at in the window. That quarter, partners were asked to clear stale opportunities out of the pipeline. You also have fct_proposal_stage_event. Quantify how much of the jump is selling and how much is the denominator changing shape, and state what you would monitor from now on.

Approach

Stop looking at the ratio and plot its parts: won value and lost value by decision week, plus counts and value landing in the excluded stages 'withdrawn' and 'no_decision'. A ratio move with a flat numerator is a denominator story.
Date the intervention and look for a spike in excluded-stage terminations around it. Those deals are not missing at random: opportunities that go stale and get cleaned up are disproportionately ones that were heading for a loss.
Reconstruct history from fct_proposal_stage_event rather than reading the mutable fct_proposal.stage column, since the cleanup overwrote the current stage in place. Identify proposals that moved from 'submitted' or 'scoping' to 'withdrawn' after the push, and how long they had been open.
Bound the effect: recompute win rate with withdrawn and no_decision counted as losses. The truth lies between that lower bound and the reported figure; report both numbers rather than picking one.
Check the second lever: is_competitive is also mutable, so reclassifying a deal as sole-sourced removes it from the rate entirely. Count flips of that flag in the window.
Propose the standing monitor: share of decided value terminating in excluded stages, published beside win rate, so the denominator can never move silently again.

Follow-up

Some withdrawals are genuine, for example a client cancelling a procurement. How would you separate those from cleanup?
What would you have asked for before the hygiene push to make this measurable afterwards?
Does the same exclusion logic distort proposal cycle time, and in which direction?

Suggested practice checklist

For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.

Day 1 — Build a fixture you can check answers against

Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.

Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.

Day 2 — Joins, filters and NULL semantics

Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.

Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.

Day 3 — Window functions and frames

Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.

Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.

Day 4 — The four analytical query patterns

Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.

Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.

Day 5 — Write SQL the way you will have to write it live

Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.

Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.

Day 6 — One day for everything that is not SQL

Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.

Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.

Day 7 — Full loop rehearsal

Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.

Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.

Getting an analysis acted on

Work that nobody used is a common and unflattering pattern in data careers, and interviewers probe for it. Have a story about an analysis that changed a decision, and be specific about how you got it in front of the person who could act. Also have one about work that went nowhere, with your reading of why.

Describe a time when you had to explain a complex machine learning model to a hardware engineer or business stakeholder who had no data science background.

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Quantify the outcome, including what you would not claim credit for.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

What did you decide not to do, and why?
How did you know the outcome was caused by your change?

Three requests, one week, and the one you defer

Medium · prioritisation · stakeholder management · scope reduction

Three requests arrive in one week. Finance needs days sales outstanding recomputed for a board meeting in four days. A partner wants a proposal win-rate model for a pursuit review in three weeks. Delivery wants a staffing forecast, with no date attached. You have one week of your own capacity and no analyst. Write the prioritisation you send back, the request you defer, and the message you send to the person whose request you defer.

Approach

Name the probe: whether you prioritise on decision dates and reversibility, or on who asked most forcefully.
Score each request on four things: what decision it changes, the date that decision is made, the cost of being late, and the cost of being wrong. A board figure has a hard date and a high cost of being wrong; a forecast with no date has neither.
Reduce scope rather than dropping work. The win-rate model is the largest piece and the easiest to get wrong, because features written after the decision, such as engagement_id and revised pricing, leak the label, and because withdrawn and no_decision proposals are not missing at random. A two-day descriptive win-rate cut by is_competitive and loss_reason answers most of what a pursuit review needs, with the model scoped separately.
Defer explicitly, with a date and a smaller substitute, rather than leaving a request to decay quietly. Silence is read as agreement and then as failure.
Put the trade-off in writing so it can be overturned by someone with more context than you have, and say what you would drop if the deferred request becomes urgent.

Follow-up

The partner escalates to the practice lead. What do you change, and what do you refuse to change?
What evidence would make you drop the finance work instead?

Answering a request to redefine the utilisation denominator

Hard · denominator integrity · restatement · escalation

Before a quarterly review, a practice lead asks for two changes to reported billable utilisation: drop consultants allocated to engagements with status = 'paused' out of the denominator, and count approved travel hours booked to client_delivery as billable. Together the changes move reported utilisation from 71 to 79 percent. You must answer in writing today. Give your answer, the principle you apply to each change separately, and what you do if you are overruled.

Approach

Name the probe: whether you can hold a measurement line by adjudicating each proposal on its merits, rather than treating the request as bad faith or accepting it to avoid a fight.
Adjudicate the paused-engagement change against what the denominator means. Billable utilisation measures the share of available consultant hours that were sold. A consultant whose engagement is paused is still available and still costs salary, which is precisely the situation the metric exists to reveal. Removing them defines away the signal, so this one is refused on definitional grounds, not on suspicion.
Adjudicate the travel change as an empirical question, because it is one. If those hours are billed to the client, is_billable and bill_rate_usd should already reflect it; if they are charged and then written off or never invoiced, counting them raises utilisation while realisation falls. Check the invoice lines for the same engagement and period rather than arguing about principle.
Quantify each change separately, and the interaction. An eight-point move made of one point and seven points is a different conversation from two four-point moves, and the lead cannot make a good decision without that split.
If a change is accepted, restate history on the new definition and publish the old series alongside it with the date of the change. A definition applied forward only makes every period-over-period comparison meaningless. If overruled, comply with the restatement and the footnote, and escalate only the part that is a misstatement of fact rather than a defensible choice.

Follow-up

Which of the two changes would you accept, and what evidence flips your answer on the other one?
The lead wants the new definition applied only from this quarter forward. Why is that worse than applying it to history, and how do you explain that in one sentence?