General Dynamics Information Technology Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at General Dynamics Information Technology, you play a critical role in transforming complex, large-scale federal, defense, and healthcare datasets into actionable insights. Your work directly supports mission-critical operations, ranging from enhancing the readiness and resilience of Special Operations Forces to conducting oversight and audits for major federal healthcare programs like the Centers for Medicare & Medicaid Services. You bridge the gap between raw data collection and strategic decision-making, helping government agencies operate more efficiently and securely.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Focus areas

• Separate novelty effects from durable behaviour change
• Decompose a metric move by segment and mix
• Define numerator, denominator and window precisely

Overview

This position demands a unique blend of technical expertise and domain-specific problem-solving. Whether you are building predictive models, analyzing prescription drug claims, or evaluating human performance metrics, your analyses drive real-world outcomes that impact national security and public wellbeing. You will collaborate closely with cross-functional teams including software engineers, biostatisticians, military personnel, and federal stakeholders to design robust data pipelines, establish metrics, and present clear analytical findings.

The work environment at General Dynamics Information Technology is characterized by high impact, scale, and professional purpose. You will navigate complex data environments while managing stakeholder expectations across both technical and non-technical audiences. While the role comes with unique challenges such as working within secure government frameworks or dealing with noisy operational data, it offers unparalleled opportunities for professional growth and direct contribution to public service.

The General Dynamics Information Technology interview process

General Dynamics Information Technology candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Recruiter Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

• Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
• Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
• Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

• Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
• Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
• Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Evaluation

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

• Whether your row counts survive each join, and whether you notice on your own when they do not
• Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
• Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
• Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

• Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
• Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
• Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Behavioral Evaluation

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

• Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
• Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
• Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

• For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
• Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
• Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

Leadership Conversations

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

• Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
• Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
• How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

• Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
• For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
• Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Where candidates lose points

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Answering a product-sense question with a list of features

Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.

Questions & practice

Describe how you would estimate confidence intervals for a population mean when working with a small sample size.

Medium · statistics and probability

Approach

1. Say what the estimate is of, and over what population it generalises.
2. Translate the result into the decision it informs, in one plain sentence.
3. Sanity-check the answer against a simple bound or a simulated case.

Follow-up

• Which assumption here is most likely to be violated in practice?
• How would you explain this result to someone who does not know statistics?

This category checks your practical knowledge of predictive modeling, feature engineering, and model validation techniques.

Medium · machine learning and modelling

Approach

1. Check what information would not exist at prediction time, and exclude it.
2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
3. Set a baseline first, so any model has something honest to beat.

Follow-up

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

Rebuild per-visitor ordering without groupby convenience methods

Easy · pandas · vectorisation · window logic

You have a DataFrame of 2 million fct_event rows with visitor_id, occurred_at_utc and event_id, unsorted and containing duplicate timestamps within a visitor. Produce three new columns: event_rank, the 1-based position of the event within its visitor ordered by occurred_at_utc; seconds_since_prev, the gap to that visitor's previous event, NULL for the first; and is_first_for_visitor. You may use sort_values, shift, cumsum, numpy and boolean masking. You may not use groupby.transform, groupby.apply, groupby.cumcount, groupby.rank or merge_asof. Break timestamp ties on event_id.

Approach

1. Sort once by ['visitor_id', 'occurred_at_utc', 'event_id'] and reset the index. The whole exercise reduces to row arithmetic on a sorted frame, and the tiebreak on event_id is what makes the result reproducible across runs.
2. Mark visitor boundaries with is_first = df['visitor_id'].ne(df['visitor_id'].shift()). This is the single fact every other column derives from.
3. Compute seconds_since_prev as the diff of the timestamp column, then overwrite it with NaT/NaN wherever is_first is True. The shift crosses the boundary between visitors and will otherwise hand the first row of each visitor the last event of the previous one.
4. Build event_rank from a running counter that resets at boundaries: take a global cumulative position (np.arange(len(df))) and subtract, per row, the global position at which that visitor started. Get the start position by forward-filling the positions where is_first is True, which is a cumsum-free reset and is O(n).
5. Verify against the forbidden method once, as a test rather than as the implementation, and confirm the two agree on every row.

Worked solution (20 min)

1. Sort on the three-key tuple and reset_index(drop=True).
2. Compute is_first via .ne(.shift()), which is True for row 0 because the shifted value is NaN.
3. pos = np.arange(len(df)); start = pd.Series(np.where(is_first, pos, np.nan)).ffill(); event_rank = (pos - start + 1).astype(int).
4. gap = df['occurred_at_utc'].diff().dt.total_seconds(); gap[is_first] = np.nan.
5. Assert event_rank equals df.groupby('visitor_id').cumcount() + 1 on the sorted frame.

Expected result: Three columns on the sorted frame: event_rank starting at 1 for every visitor and increasing by 1 with no gaps, seconds_since_prev null exactly where is_first_for_visitor is True, and is_first_for_visitor summing to df['visitor_id'].nunique().

Verify it yourself

• is_first_for_visitor.sum() equals the number of distinct visitor_id values.
• seconds_since_prev.isna().sum() equals that same number.
• event_rank.max() per visitor equals that visitor's row count, and event_rank is non-negative everywhere.
• A visitor with two events at the identical timestamp gets ranks 1 and 2 with a gap of 0.0, ordered by event_id.

Follow-up

• The frame does not fit in memory. How does your approach change if you can only process one visitor-partitioned chunk at a time?
• occurred_at_utc is client-supplied and sometimes runs backwards within a visitor. Does your seconds_since_prev go negative, and should it?
• How would you extend this to reset the counter at every change of surface as well as visitor?

How would you identify duplicate records across a massive healthcare claims database using advanced SQL grouping and filtering?

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

• How would you verify this result without re-running the same query?
• What breaks if events arrive late or out of order?

Explain how you would optimize a slow-running SQL query that joins multiple tables containing millions of rows.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. 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?

Weekly visit-to-signup conversion split by acquisition channel

Easy · funnel · conversion · aggregation

From fct_session (session_id, visitor_id, started_at_utc, referrer_channel, is_bot_flagged, consent_state) and fct_event (visitor_id, occurred_at_utc, event_name), compute visit-to-signup conversion for one ISO week, split by channel. session_id is the unique key of fct_session. Denominator: distinct visitor_id with a session starting in the week, is_bot_flagged = FALSE and consent_state <> 'denied'. Numerator: those visitors with a 'signup_completed' event in the same week. Label each visitor with the referrer_channel of their first session in the window. Return channel, visitors, signups and rate, plus one all-channel total row.

Approach

1. Build a visitor spine that is one row per visitor: filter sessions to the week, drop is_bot_flagged and consent_state = 'denied', then take the first session per visitor with ROW_NUMBER() OVER (PARTITION BY visitor_id ORDER BY started_at_utc, session_id) = 1 to carry the channel label. Collapsing to one row here is what makes the channel buckets mutually exclusive and the totals additive.
2. session_id is the unique key, so that ordering is total and the label is reproducible. If the table carried no unique key you would have to write an explicit tie rule instead, because two sessions on different channels at the identical timestamp would otherwise label the visitor differently between runs.
3. Attach the outcome as a semi-join (EXISTS on a signup_completed event for that visitor inside the same week) rather than a join to the event table, so a visitor who fires the event twice does not count twice and inflate the numerator past the denominator.
4. Aggregate with COUNT() as visitors and COUNT() FILTER (WHERE signed_up) as signups, and compute the rate as signups::numeric / NULLIF(visitors, 0) so an empty channel returns NULL rather than a division error.
5. Produce the total with GROUP BY GROUPING SETS ((channel), ()), which re-sums numerator and denominator for the total row. Averaging the channel rates gives a different and wrong number whenever channel volumes differ, which they always do.
6. Verify the spine before trusting the output: COUNT(*) must equal COUNT(DISTINCT visitor_id), and the per-channel visitor counts must sum to the total row.

Worked solution (20 min)

1. Write the filtered session CTE and check its row count against an unfiltered count, so you know how much volume the bot and consent filters removed.
2. Add the ROW_NUMBER first-session pick and assert one row per visitor.
3. Add the EXISTS outcome flag and aggregate with FILTER.
4. Add GROUPING SETS for the total and format the rate to four decimal places.
5. Spot-check one channel by hand: pull its visitor list, count signups directly, compare.

Expected result: One row per referrer_channel plus a total row, with visitors, signups and a decimal rate. Channels with zero signups appear with rate 0, not missing. The total rate lies strictly between the minimum and maximum channel rate unless one channel holds all the volume.

Verify it yourself

• Per-channel visitor counts sum exactly to the total row's visitor count.
• COUNT(*) equals COUNT(DISTINCT visitor_id) on the spine CTE.
• Replacing the EXISTS with a plain inner join changes the signup count only if duplicate signup_completed events exist; if it does change, say by how much.
• Every rate is between 0 and 1.
• Re-running the first-session pick with session_id removed from the ORDER BY returns the same labels; if it does not, visitors hold same-timestamp sessions on different channels and the tie rule is doing real work.

Follow-up

• The denominator is distinct visitors. If a browser release shortens cookie lifetime, what happens to this rate, and how would you tell that apart from a genuine drop?
• A visitor's first session is direct and their signup session is paid search. Your label says direct. When is that the wrong answer for the decision being made?
• How do you roll four weeks into a month, and why is averaging the four weekly rates wrong?

How do you balance quantitative metric improvements with qualitative user feedback when evaluating program effectiveness?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Name one primary metric, then the guardrail that stops it being gamed.
3. 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?

This category tests your ability to translate business and mission goals into quantifiable metrics and evaluate product or program success.

Medium · product sense and metrics

Approach

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

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?

If a key performance indicator drops by fifteen percent week-over-week, what step-by-step framework do you use to diagnose the root cause?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. State what result would change your recommendation, so the answer is falsifiable.

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?

What steps do you take when an A/B test shows statistically significant results for secondary metrics but not the primary metric?

Medium · product sense and metrics

Approach

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

Follow-up

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

Explain how you determine the required sample size and statistical power before launching an experiment.

Medium · a/b testing and experimentation

Approach

1. Say whether units interfere with each other, and switch design if they do.
2. State the primary metric and the minimum effect worth shipping, then size the test.
3. Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

• How would you handle interference between treated and control units?
• What would you conclude if the result is positive but the test is underpowered?

What are some common experimentation pitfalls, such as sample ratio mismatch or peeking, and how do you avoid them?

Medium · a/b testing and experimentation

Approach

1. Name the guardrails that would stop a launch even on a positive primary result.
2. Name the randomisation unit first; it decides the variance and what the test can detect.
3. Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

• What would you do if you could not randomise at all?
• How would you handle interference between treated and control units?

A completion rate the owning team can move without fixing anything

Medium · gaming · instrumentation · funnel

A team's target is core-flow completion rate: distinct fct_event.flow_instance_id with a 'flow_completed' event within 30 minutes of its 'flow_started' and no 'error_shown' carrying the same flow_instance_id in between, over distinct flow_instance_id with a 'flow_started' in the window, split by surface and app_version. The same team owns the client that emits those events and the tracking plan that defines them. List the ways this rate rises without any user completing more flows, then redefine the metric and its guardrails so those routes are closed. Deliverable: the hardened definition.

Approach

1. Work the emission side first, because that is what the team controls: delay minting flow_started until after the first screen so the highest-dropping attempts leave the denominator; stop emitting or rename error_shown; mint a fresh flow_instance_id on each retry so one failed attempt becomes several attempts whose last one completes; move flow_completed earlier in the flow.
2. Sort those moves by where they are visible. None of them shows in the rate itself; three of them show only in volume, which is why the denominator has to be published on the same chart as the rate.
3. Re-anchor the numerator on something outside the flow's own instrumentation: require a downstream is_core_action = TRUE event for the same user_id within 30 minutes of flow_completed, so a completion only counts when it produced the thing the flow exists to produce.
4. Add the guardrail that catches the retry route specifically: mean and p90 flow_instance_id per user per day, with the rule written down that a rising completion rate alongside rising attempts per user is a regression and not a win.
5. Make definition changes visible instead of forbidden: stamp a tracking-plan version on the series and re-base the history when event semantics or the 30-minute window change, rather than splicing two definitions into one line.

Worked solution (25 min)

1. Enumerate at least four emission-side moves and mark for each whether it shows in the rate or only in volume.
2. Pin the denominator: publish flow_started per 1,000 sessions and per distinct user beside the rate.
3. Rewrite the numerator to require a downstream core action for the same user within 30 minutes.
4. Add the attempts-per-user guardrail with its interpretation rule stated as a sentence.
5. Version the definition and write the re-base policy for the historical series.

Expected result: A hardened definition in which each enumerated gaming route is either blocked by the numerator, exposed by a published volume series, or forced to surface as a definition version change.

Verify it yourself

• Replay one untouched historical week under the old and hardened definitions. A large gap means the hardening changed the meaning of the metric, not only the incentives around it.
• flow_started per 1,000 sessions should be roughly continuous across an app_version boundary unless the flow's entry point genuinely moved; a step change there is an instrumentation change.
• Attempts per user and completion rate should not rise together within one build.

Follow-up

• How do you distinguish a genuine instrumentation fix from gaming, given both look like a step change confined to one release?
• The jump appears in exactly one app_version. Does that exonerate the team or implicate it?
• What do you do with eighteen months of history once the definition is hardened?

Gross revenue churn doubled with no cancellations behind it

Hard · billing-periods · fx · revenue-churn · decomposition

Gross monthly revenue churn computed from fct_subscription_period doubled from 1.8% to 3.6% in one month. The support queue shows no rise in cancellations and renewals look normal. You have fct_subscription_period with subscription_id, account_id, period_start_utc, period_end_utc, mrr_cents, mrr_cents_constant_fx, seats_billed, period_status, change_reason and canceled_at_utc, plus dim_account. Decompose the 1.8-point rise into named mechanisms, size each in points of the headline, and state the remainder you cannot explain.

Approach

1. Reconstruct the numerator row by row and group it by change_reason before arguing about causes. A mid-period plan or seat change closes the current period row and opens a new one, so any implementation that treats a closed row as lost revenue books upgrades, downgrades and seat changes as churn; the change_reason breakdown of the numerator makes that visible in one query.
2. Check the recognition timestamp. Churn belongs at period_end_utc, because revenue continues until the period ends, not at canceled_at_utc when the button was pressed. Then count period_end_utc rows per month across thirteen months: annual cohorts concentrate their period ends in the month twelve months after they were signed, so a spike that repeats in the same month last year is seasonality in the book, not an event this month.
3. Recompute the whole numerator and denominator on mrr_cents_constant_fx. If the constant-currency figure is materially flatter, the move is an exchange-rate translation and belongs nowhere in a churn narrative.
4. Check period_status handling. Rows with period_status = 'past_due' are dunning, not cancellation; a status-based rule counts them as loss while a period-end rule does not, and a dunning backlog can move the number by itself.
5. Express every mechanism in points of the headline, sum them, and print the residual explicitly next to the month-to-month standard deviation of the prior twelve months. A decomposition without a stated remainder is a story, not an accounting.

Follow-up

• Which of these mechanisms should be fixed in the metric definition and which should be reported as a genuine business fact?
• How would you present a month whose churn is dominated by an annual cohort anniversary without the audience concluding the business is deteriorating?
• What would you change so that an upgrade can never enter the churn numerator again, and how would you test that it worked?

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.

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

This category focuses on your communication, teamwork, project ownership, and alignment with professional expectations.

Medium · behavioural and stakeholder questions

Approach

1. State the situation in two sentences and spend the rest on your reasoning.
2. Close with what you would do differently, concretely.
3. Quantify the outcome, including what you would not claim credit for.

Follow-up

• What did you decide not to do, and why?
• What would you do differently if you ran that project again?

Explain a wide interval to a non-technical executive

Medium · communication · uncertainty · pricing

A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

Approach

1. Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
2. Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
3. Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
4. Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
5. Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.

Follow-up

• The executive says to give the midpoint and they will manage the risk. What do you do?
• How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
• Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?

Handle a request for numbers supporting a decision already made

Hard · integrity · framing · stakeholder

A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

Approach

1. Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
2. Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
3. Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
4. Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
5. Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
6. Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.

Follow-up

• The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
• What changes if the honest analysis says the sunset is clearly the wrong call?
• How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?