As a Data Scientist at Safaricom, you sit at the heart of East Africa’s digital transformation. Safaricom is not just a telecommunications company; it is a massive technology ecosystem driven by M-PESA, financial services, enterprise solutions, and high-speed connectivity. In this role, you will be responsible for translating petabytes of transactional, network, and customer data into actionable models that directly impact over 40 million subscribers.
The work you do has immediate, real-world consequences. Whether you are building credit scoring algorithms for financial inclusion products like Fuliza and M-Shwari, optimizing network traffic to prevent outages, or designing hyper-personalized customer campaigns, your models will run at an unprecedented scale. You will collaborate closely with product managers, software engineers, and business leaders to turn raw data into strategic assets.
This role requires a rare blend of deep technical expertise and strong business acumen. You must be comfortable navigating complex, sometimes ambiguous data landscapes while maintaining a clear focus on delivering value to the end user. It is a challenging but highly rewarding position where your insights can shape the economic landscape of the entire region.
Online Application
reportedThis 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
Timed Technical Assessment
reportedMuch 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
Behavioral Screening
reportedBehavioural 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.
Technical and Managerial Panel Interview
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
PracHub editorial advice for the preparation topics above.
Recalibrating an underwriting cutoff on approved and funded applicants only
Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.
Counting authorizations instead of weighting them, and summing amounts across currencies
Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.
Analysing at a different unit than the one randomised
Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you handle class imbalance when building a predictive model …
How would you handle class imbalance when building a predictive model for fraud detection?
Approach
- Set a baseline first, so any model has something honest to beat.
- 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.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- What would you monitor after launch to know the model is still valid?
Share an experience where your model did not perform as expected in pr…
Share an experience where your model did not perform as expected in production. What steps did you take to diagnose and resolve the issue?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Say how the offline result would be validated online before it is trusted.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- Where could label leakage enter this setup?
- What would you monitor after launch to know the model is still valid?
Given an array of integers representing network latency logs, write an…
Given an array of integers representing network latency logs, write an efficient Python algorithm to find the longest contiguous sub-segment of stable latency.
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- 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?
- What would you monitor after launch to know the model is still valid?
Measure calibration of a twelve-month default probability from scratch
fct_loan_application gives application_id, model_pd_12m, model_version, decision, funded_at and loan_id. fct_loan_performance_monthly gives loan_id, months_on_book, days_past_due and charge_off_flag. Define the outcome as ever 90 or more days past due, or charged off, by months_on_book = 12. Without sklearn or scipy, build an equal-count binned reliability table, the expected calibration error, the Brier score and its reliability, resolution and uncertainty components, and report the residual the binned identity leaves behind. Restrict to cohorts that have actually reached 12 months on book.
Approach
- Build the label first and name the population it covers out loud: only funded loans have outcomes, so this measures calibration on the approved population. The declined region is unmeasured, and no binning scheme repairs that.
- Restrict to applications whose loans have reached months_on_book = 12. A cohort observed at 8 months has a mechanically lower default rate and will read as systematic over-prediction that is really just immaturity.
- Bin by equal count, deciles of model_pd_12m through a rank-based cut, not equal width. The PD distribution is heavily right-skewed, so equal-width bins put most of the mass in the first bin and leave the risky bins with single-digit counts whose observed rates mean nothing.
- Per bin compute n, mean predicted, observed rate, and the binomial standard error sqrt(o(1-o)/n) so a gap can be read against noise. ECE is the count-weighted mean absolute gap between mean predicted and observed.
- Compute Brier directly as the mean squared error, then reliability = sum of n_k (pbar_k - obar_k)^2 over N, resolution = sum of n_k (obar_k - obar)^2 over N, uncertainty = obar(1 - obar). Report residual = Brier - (reliability - resolution + uncertainty). That identity is exact only for discrete forecasts, so with binned continuous scores the residual is the within-bin spread of the score; a large one means the bins are too wide to support the decomposition.
- Split by model_version. A mixed-version population can look well calibrated in aggregate while each version is biased in opposite directions.
Worked solution 45 min
- Reduce fct_loan_performance_monthly to one row per loan_id with the maximum days_past_due and any charge_off_flag over months_on_book 0 to 12, plus the maximum months_on_book observed, and keep only loans reaching 12.
- Inner-join to approved and funded applications, and record how many approved applications were dropped for immaturity and how many decisions were declines that never enter the measurement at all.
- Assign deciles with a rank-based cut on model_pd_12m, then aggregate n, mean predicted, observed rate and standard error per bin.
- Compute ECE, Brier, reliability, resolution, uncertainty and the residual, and print all six.
- Repeat the whole computation split by model_version and compare the per-version reliability against the pooled figure.
Follow-up
- AUC is unchanged after a population shift but the reliability curve has moved. What happened, and what do you do about it?
- How would you recalibrate without retraining, and what would you check afterwards?
- The top decile shows observed default well above predicted. Is that a calibration problem or a policy problem?
How do you optimize a SQL query that involves joining multiple multi-m…
How do you optimize a SQL query that involves joining multiple multi-million row tables?
Approach
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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?
Write a SQL query to calculate the month-over-month growth rate of act…
Write a SQL query to calculate the month-over-month growth rate of active M-PESA users.
Approach
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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?
Write a Python script to parse, clean, and aggregate unstructured tran…
Write a Python script to parse, clean, and aggregate unstructured transactional logs.
Approach
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
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?
Collapse merchant retry chains before measuring authorization approval
Merchant retry logic turns one declined purchase into several rows in fct_payment_authorization. Collapse attempts into chains: inside a partition of (card_token_id, merchant_id, amount_minor), consecutive rows whose requested_at gap is 15 minutes or less belong to the same chain. Return one row per chain with chain_started_at, attempt_count, amount_minor, channel, and chain_outcome set to 'approved' when any attempt in the chain was approved. Then report a 7-day rolling dollar-weighted approval rate by channel over chains rather than attempts. Exclude is_reversal rows.
Approach
- In a CTE, take LAG(requested_at) OVER (PARTITION BY card_token_id, merchant_id, amount_minor ORDER BY requested_at) and set is_new_chain when the previous value is null or the gap exceeds 15 minutes.
- Turn the flag into a chain identifier with SUM(is_new_chain::int) OVER (same partition, same order, rows unbounded preceding to current row), which is the standard gaps-and-islands construction.
- Aggregate to one row per chain: MIN(requested_at), COUNT(*), and BOOL_OR(auth_result = 'approved') as the chain outcome, because the purchase succeeded if any attempt in the chain did.
- Roll the chains to a daily grain per channel, then apply the rolling window as PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW. An interval-offset RANGE frame is value-based: it admits every row in the partition whose chain_date falls in the closed window [D - 6 days, D], so a channel with no chains on some dates still gets the right seven-day sums — sparsity is exactly what this construct tolerates. ROWS BETWEEN 6 PRECEDING AND CURRENT ROW is the one that needs a dense grain, because it counts rows and on a sparse grain reaches back further than seven days.
- Left-join a dense date spine only if the output needs a row on days with no chains. That is a presentation requirement, not a correctness one, and it leaves every rolling sum unchanged. The RANGE frame's preconditions are that chain_date is a date or timestamp (an interval offset needs an ordering column it can be added to) and that the aggregation left one row per (channel, chain_date).
- Divide approved chain value by total chain value inside the rolling frame, never the average of daily rates, since averaging rates weights a quiet day the same as a heavy one.
Worked solution 40 min
- CTE 1: filter out is_reversal rows, then compute prev_requested_at with LAG and the is_new_chain flag.
- CTE 2: build chain_id with a running SUM of the flag over the same partition and order.
- CTE 3: group by partition keys plus chain_id to emit chain_started_at, attempt_count, amount_minor, channel and chain_outcome.
- CTE 4: aggregate chains to (chain_date, channel) with approved value and total value. Add the date spine here only if the report must show empty days.
- Final SELECT: two rolling SUMs over PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW, divided at the end.
Follow-up
- This definition chains off the previous attempt, so a chain can span an hour if attempts arrive every 14 minutes. When is that right, and when would you anchor the window to the first attempt instead?
- Two genuine purchases of the same amount at the same merchant, four minutes apart, collapse into one chain. How large is that error and how would you bound it?
- A channel goes dark for three days mid-window. Which of the two frame types would silently widen, and what would the resulting rate look like?
- What changes if the merchant retries with a slightly different amount to dodge a velocity rule?
Monitor a rollout daily without inflating false positives
A risk-rule change is ramped to 50 percent and the team reads the dashboard every morning for twenty business days, intending to stop the first time the two-sided p-value on the count-weighted approval rate falls below 0.05. Quantify how much that inflates the false positive rate, propose a monitoring scheme that still permits an early stop for harm, and explain what dispute maturity does to the matured fraud basis points guardrail when it is read on day twenty.
Approach
- Quantify rather than assert. Repeatedly applying a fixed-horizon test at nominal two-sided 0.05 inflates the family-wise type I error to roughly 8 percent at 2 looks, 14 percent at 5, 19 percent at 10 and about 25 percent at 20 equally spaced looks. Under continuous monitoring with no stopping rule the probability of crossing at some point tends to 1.
- Pick a scheme matched to how the team actually behaves. If looks are on a fixed schedule, use a group-sequential design with an alpha-spending function: O'Brien-Fleming spends almost nothing early so the final boundary stays near nominal, which suits a team that mostly wants to ship at the end; Pocock spends evenly and buys genuine early-stopping power at the cost of a stricter final boundary. If looks are truly continuous and ad hoc, use an always-valid confidence sequence such as a mixture sequential probability ratio test, which is valid at every moment in exchange for a larger fixed-horizon sample at equal power.
- Treat the harm stop as a separate, asymmetric decision. Stopping a change because it looks harmful costs one abandoned experiment; failing to stop costs real loss every day it runs. Run the guardrail as a one-sided monitor at a looser alpha with its own pre-registered stop rule, and do not spend the primary metric's alpha budget on it.
- Fix the schedule before launch: the number of looks, their timing, the boundaries, and the maximum sample. Boundaries computed after the fact from however many times someone opened the dashboard are not a correction.
- Separate peeking from immaturity on the fraud guardrail. Disputes on a transaction can be filed for roughly 120 days, and some reason codes run longer, so a day-twenty read covers transactions with at most twenty days of dispute exposure. The number is not low, it is incomplete. Either report only matured transaction months, or apply development factors estimated from completed months and label the result an estimate with its interval.
Worked solution 25 min
- Quote the inflation for the stated plan: twenty looks at nominal two-sided 0.05 gives a family-wise type I error of about 25 percent, so one rule change in four would appear significant with no true effect.
- Specify the replacement: five pre-scheduled looks at 20, 40, 60, 80 and 100 percent of planned sample under an O'Brien-Fleming spending function, with z boundaries of approximately 4.56, 3.23, 2.63, 2.28 and 2.04. For comparison, a Pocock design at five looks uses a constant boundary of about 2.41.
- Add a one-sided harm monitor on the fraud guardrail and the decline-rate guardrail with its own alpha and its own pre-registered rule, documented before launch.
- For the fraud read, restrict to transaction months with at least 120 days of maturity; if none exist yet, present a development-factor estimate from completed months with its uncertainty and mark the recent months incomplete on the chart rather than plotting them as low.
- If the boundary is crossed early, report a bias-adjusted effect estimate, because the estimate at a stopping boundary is systematically larger in magnitude than the truth.
Follow-up
- Under an O'Brien-Fleming boundary you cross on day three. What do you say about the effect size, and why is the naive point estimate biased?
- How would you set the stopping rule for the fraud guardrail given that its true value is not observable inside the test window?
- The team argues that they are only looking, not deciding, so peeking is harmless. Under what precise condition is that true, and how would you verify it?
Read a segment-sliced result with a fading first-week lift
A three-week test of an in-app credit-limit-increase offer had one pre-registered primary metric. The readout also slices 24 secondary segment-by-metric cells, of which three are significant at 0.05 with p-values 0.011, 0.028 and 0.041. The treatment effect on the primary was plus 8.1 percent in calendar week one, plus 3.4 percent in week two and plus 1.5 percent in week three. Say which claims survive, what the decay does and does not prove, and what you tell the sponsor.
Approach
- Compute what chance alone produces before interpreting any cell. With 24 tests at 0.05 the expected number of false positives is 1.2, the probability of at least one is 1 - 0.95^24 = 0.708, and the probability of three or more under an independence approximation is about 0.12. Three hits is an unremarkable outcome, not a signal.
- Apply a stated correction rather than a vibe, and be precise about which one the reported numbers can settle. Holm and Bonferroni both begin at 0.05 / 24 = 0.00208, so the smallest p-value alone decides them. Benjamini-Hochberg sorts all m p-values ascending and finds the largest k with p(k) <= k * q / m; at q = 0.10 and m = 24 the rank-k threshold k / 240 starts at 0.00417 and rises to 0.10 at k = 24, so it is a step-up test over the whole family and its verdict cannot be read off the three cells that happened to fall below 0.05. Ask for all 24 p-values, report the surviving set, which may be empty, and demote the rest to hypotheses for a future test.
- Refuse to call the decay novelty until the competing explanations are ruled out. Three things produce a falling weekly effect: a genuine novelty response from existing users that reverts, a mix shift because week three's exposed population is not week one's, and an effect that is correctly one-shot because a limit increase is taken once per customer.
- Separate them with concrete cuts. Plot the effect against time since first exposure instead of calendar week, which removes the mix shift. Compare users onboarded during the test, who never saw the previous experience and therefore cannot show novelty, against existing users. Hold back a small fraction of traffic for a long-run read.
- Fix the estimand to match the mechanism. For a one-shot offer the meaningful quantity is cumulative take-up per exposed user over a fixed horizon, not a weekly rate, and under that estimand a declining weekly rate is exactly what a working feature looks like.
- Give the sponsor one number with its interval, name the decision it supports, and list the exploratory cells separately under a heading that says they are not results.
Follow-up
- The three significant cells are all in the same segment. Does that change your reading, and how would you test whether it is a real interaction rather than three correlated slices of one population?
- Design the long-term holdback: what size, for how long, and what does it cost you in foregone treatment?
- If cumulative take-up is the estimand, what is the right horizon and how do you keep it from being chosen after seeing the data?
Fraud losses appear to halve in recent transaction months
A weekly chart attributes fct_card_dispute cases to the requested_at month of the linked fct_payment_authorization row. The two most recent months show the first-chargeback rate falling by half, and a risk rule shipped six weeks ago. Columns: dispute_id, auth_id, dispute_category, dispute_stage, opened_at, disputed_amount_minor, liability_shift_flag, outcome, net_loss_minor, resolved_at. Decide whether the rule worked, and produce the version of the chart you would sign your name to.
Approach
- Separate the two dates explicitly. opened_at is when a case was filed, requested_at is when the transaction happened. Attributing by transaction month is the right causal choice and is exactly what makes the newest months structurally incomplete.
- Measure the filing lag rather than assuming it: the distribution of opened_at minus requested_at over fully developed months, split by dispute_category, and the age at which around 95 percent of cases have arrived.
- Build a development triangle of transaction month by months of development on cumulative case counts, and estimate age-to-age factors from the columns that are complete.
- Develop the immature months with those factors and plot the result as an estimate with a visible band, kept visually distinct from the matured series rather than blended into it.
- State the assumption the method needs: a stable development pattern across cohorts. A change in filing behaviour, merchant mix or the dispute team's own backlog breaks it, so inspect factor stability down each column before relying on the estimate.
- Only then evaluate the rule, comparing pre-change and post-change cohorts at equal development age.
Follow-up
- What leading indicator would you accept while the cohort matures, and what is its known bias?
- How does liability_shift_flag change which disputes you should expect to see in the first place?
- If the rule also blocked good transactions, where does that cost appear, and is any of it in this chart?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
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 statistical model to…
Describe a time when you had to explain a complex statistical model to a non-technical business stakeholder. How did you ensure they understood?
Approach
- Quantify the outcome, including what you would not claim credit for.
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
Tell me about a project where you had to manage competing priorities f…
Tell me about a project where you had to manage competing priorities from different business units. How did you resolve the conflict?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Pick a story where you drove the decision, not one where you observed it.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
Recommend a decision whose true outcome matures a year later
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
Approach
- Fix the readout before the rollout, because a readout chosen after the data arrives is a story rather than a decision rule: indicator, window, threshold and reversal condition all go in writing first.
- Choose the leading indicator on its measured relationship to the matured outcome in historical vintages rather than on availability. Early delinquency, typically the share reaching dpd_1_29 or missing a first scheduled payment by months_on_book 3, is the usual candidate, and you quantify how well it predicted the 12-month rate across past cohorts.
- State the bias and its direction plainly: early delinquency under-represents default that emerges later and is contaminated by servicing and payment-date effects, so treat it as a floor on risk rather than an estimate of it.
- Buy identification where full randomisation is unavailable: a narrow randomised approval band around the cutoff, or a staged rollout by channel or region read as a difference-in-differences, with the parallel-trends assumption stated and checked in the pre-period rather than assumed.
- Give the executive the binary they asked for with the trigger attached in the same sentence: yes, conditional on the month-3 indicator staying inside a stated band, with an automatic hold if it breaches.
Follow-up
- How would you validate that the month-3 indicator predicts the 12-month outcome, and what evidence would invalidate it mid-rollout?
- Compliance refuses a randomised band. What is your next-best identification strategy, and what precision do you lose by taking it?
- 01
Describe a time when you had to explain a complex statistical model to a non-technical business stakeholder. How did you ensure they understood?
- 02
Tell me about a project where you had to manage competing priorities from different business units. How did you resolve the conflict?
- 03
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
Is this an official Safaricom interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Safaricom. Rounds and questions reflect what candidates have reported, not a process Safaricom has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical timeline for the Safaricom hiring process?
The entire process, from application to offer, generally takes between 4 to 8 weeks. This timeline can vary depending on the availability of the interview panels and the specific department's hiring urgency.
PracHub interview research ↗How difficult is the technical assessment on Codility?
Candidates generally rate the technical test as moderate to difficult. It requires a solid understanding of algorithmic problem-solving and efficient SQL querying under time constraints. Practicing on competitive coding platforms is highly recommended.
PracHub interview research ↗Does Safaricom offer hybrid or remote work options for Data Scientists?
Yes, Safaricom operates on a hybrid work model, allowing employees to balance remote work with in-office collaboration at their headquarters in Nairobi, depending on team requirements.
PracHub interview research ↗What distinguishes successful candidates in the final panel interview?
Successful candidates are those who can connect their technical solutions directly to business outcomes. They demonstrate a clear understanding of Safaricom's business model and show strong leadership and communication skills.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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