Most data scientist interview processes span 4–6 stages: (1) recruiter screen (background and motivation), (2) technical phone screen (SQL, Python, or statistics questions — 45–60 min), (3) take-home or live coding exercise (data analysis, model building, or case study), (4) on-site or virtual final round with 3–5 interviews covering statistics, ML, product analytics, and behavioural questions. Some companies (Meta, Netflix) include a separate "metrics and A/B testing" interview. The process typically takes 3–8 weeks.

Data analysts typically focus on describing and explaining what happened using SQL, dashboards, and statistical summaries. Data scientists go further: building predictive models, designing experiments, and applying machine learning to improve products or automate decisions. In practice, the distinction varies by company — at some organisations "data scientist" is effectively a senior analyst role; at others (Google, Meta, Netflix) it involves substantial ML engineering. Always check the job description carefully and ask the recruiter where this role sits on that spectrum.

Core topics: probability distributions (binomial, Poisson, normal), hypothesis testing (t-test, chi-square, Mann-Whitney), confidence intervals, p-values and statistical power, Bayesian vs frequentist thinking, A/B test design (sample size calculation, stopping rules), regression (OLS, logistic), and common pitfalls (multiple comparisons, selection bias, survivorship bias, Simpson's paradox). For senior roles, add causal inference methods (DiD, IV, RD designs) and more advanced ML theory.

Meta DS interviews are heavily product analytics-focused: you'll be assessed on your ability to define and move product metrics, design A/B tests, and diagnose metric drops. SQL, Python, and business intuition are weighted heavily. Google DS interviews tend to be more statistics and ML-intensive, with deeper emphasis on algorithmic thinking and data at scale. Both companies value rigorous experimental design and an ability to connect analysis to business decisions. See the Google Data Scientist and Meta Data Scientist guides for company-specific prep.

No — most data scientist roles at top tech companies do not require a PhD. A strong foundation in statistics, machine learning, and programming (Python and SQL) is far more important than academic credentials. That said, research scientist roles (at DeepMind, OpenAI, Anthropic) typically do expect PhD-level work. For product DS or applied ML roles, a Master's or even a Bachelor's with strong project experience is typically sufficient. Build a portfolio of real projects and Kaggle competition results to demonstrate competence.

In the US, data scientist compensation ranges widely by company and seniority. Entry-level DS: $120k–$180k total comp. Mid-level: $160k–$280k. Senior DS: $220k–$380k. Staff DS or Data Science Manager: $300k–$500k+. FAANG and elite tech companies (Stripe, Airbnb, Anthropic) pay at the top end, with significant RSU components. Outside of top-tier tech, expect 20–40% lower total comp. In the UK, senior DS roles at major tech companies range from £90k–£160k total comp. Data science continues to command a salary premium over general software engineering at many companies.

For a senior role at a top company: 6–10 weeks of structured preparation. Spend the first two weeks on statistics and probability fundamentals, the next two weeks on SQL practice (LeetCode SQL, StrataScratch), then two weeks on ML theory and case studies, and the final two weeks on mock interviews. For entry-level roles, 4–6 weeks focused on SQL, basic statistics, Python (Pandas, NumPy), and a strong portfolio project is typically sufficient. Prioritise the skills most heavily weighted by your target company — check job postings and Glassdoor reports for clues.