I've been recruiting a Senior Data Scientist role this month. Here is a transparent overview of the interview process for a Senior Data Scientist role, including role fit, causal inference case studies, and leadership discussions. This is designed to help candidates understand what to expect and prepare thoughtfully for senior-level, product led data science interview.
This repository provides a transparent overview of the interview process for a Senior Data Scientist role, with a particular focus on causal inference, experimentation, and leadership in a product-led environment.
The aim is to give candidates clarity on what to expect at each stage, how they are assessed, and how to prepare — reducing uncertainty and helping people perform at their best.
Purpose
Assess alignment with the role and evaluate experience against key role competencies.
What “good” looks like
Strong candidates typically demonstrate:
- Clear motivation aligned with what the role can offer
- Relevant past experience mapped to the competencies required for the role
- Thoughtful reflection on impact, trade-offs, and learning
Interviewers
Hiring manager or senior team member.
Preparation guidance for candidates
This stage explores your interests, motivations, and prior experience in more detail.
You should expect:
- Competency-style questions
- Follow-ups that probe depth, ownership, and decision-making
Preparation tips:
- Bring concrete examples from your experience
- The STAR framework (Situation, Task, Action, Result) is a helpful way to structure responses
- Focus on your contribution and impact
Stage 2 — Causal Inference Case Study (70 minutes) - (Data set would be sent to the candidate to present and analyse)
Purpose
To assess experience and reasoning across causal inference methods, both experimental and observational.
What “good” looks like
Strong candidates typically demonstrate:
- A solid understanding of the steps involved in designing causal experiments
- Awareness of limitations of traditional A/B testing and how to adapt when experiments fail or are not possible
- Familiarity with observational causal inference methods and their assumptions
- Ability to communicate uncertainty and limitations clearly
Interviewers
Two/Three senior data scientists or data science leaders.
Scenario
You are a data science manager supporting a customer experience product team.
The team has developed a new feature for a shopping app: Smart Reorder Suggestions.
This feature uses customers’ purchase history and time since last order to suggest items they’re likely to need soon.
The goals are to:
- Increase basket order size
- Improve customer retention (repeat orders)
The product team wants to understand whether this feature causally improves these outcomes and whether it should be rolled out more widely.
Areas explored (Part 1)
- Choice of unit of randomisation (user, session, household)
- Primary and secondary metrics (e.g. basket size, order frequency, retention)
- Effect size and links to business outcomes
- Power, sample size, and sequencing considerations
- Exposure, contamination, and interference risks
- Selection bias, attrition, and adverse effects
- Randomisation strategies (simple vs stratified)
- Statistical validity checks and guardrail metrics
- Decision rules and recommendations
Areas explored (Part 2)
- Recognition of compromised internal validity
- Use of post-hoc adjustment methods:
- Regression adjustment
- Propensity score matching or weighting
- Reweighting or stratified re-analysis
- Awareness of residual bias and deeper causes of failure
- Clear communication of uncertainty and limitations
Areas explored (Part 3)
- Observational causal methods:
- Difference-in-Differences
- Synthetic controls
- Interrupted Time Series
- Matching and quasi-experimental approaches
- Assumptions (e.g. parallel trends, SUTVA)
- Sensitivity analyses and robustness checks
- Seasonality and external shocks
Areas explored (Part 4)
- Use of causal diagrams (DAGs)
- Identifying confounders vs mediators and colliders
- Appropriate control strategies
- Interpretation and communication of results and caveats
Purpose
Assess leadership capability, ability to drive impact through others, and alignment with company values and culture.
What “good” looks like
Strong candidates typically demonstrate:
- Experience leading teams to deliver meaningful outcomes
- Thoughtful approaches to coaching, decision-making, and influence
- Clear alignment with values such as trust, learning, and collaboration
Interviewers
Senior stakeholders and cross-functional leaders.
This process is designed to assess not just technical depth, but judgement, communication, and leadership.
Candidates are not expected to be perfect — the focus is on structured thinking, clarity of reasoning, and the ability to explain trade-offs in complex, real-world situations.