JOB TITLE: Senior Data Scientist – Measurement EXPERIENCE: 5–7 Years
DOMAIN: Data Science (Measurement)
JOB SUMMARY
We are looking for a Senior Data Scientist specializing in measurement science, experimentation, statistical inference, and causal modeling. The ideal candidate will have strong experience designing and analyzing experiments, measuring the impact of business interventions, and applying causal inference techniques to support evidence-based decisions.
The candidate will lead measurement initiatives involving A/B testing, statistical power analysis, experimental design, quasi-experimental methods, Difference-in-Differences (DiD), Propensity Score Matching (PSM), and causal modeling.
KEY RESPONSIBILITIES
A. EXPERIMENTATION AND A/B TESTING
1. Design, implement, and analyze A/B tests and controlled experiments.
2. Define hypotheses, primary and secondary metrics, guardrail metrics, treatment groups, and control groups.
3. Perform sample size calculations, statistical power analysis, minimum detectable effect (MDE) estimation, and experiment duration planning.
4. Apply appropriate statistical tests and confidence intervals to estimate treatment effects.
5. Identify experimentation issues such as sample ratio mismatch, selection bias, contamination, novelty effects, and repeated testing.
6. Establish best practices for experimentation design, analysis, and reporting.
B. QUASI-EXPERIMENTAL DESIGN AND CAUSAL INFERENCE
1. Apply quasi-experimental methods when randomized controlled experiments are not feasible.
2. Design and implement Difference-in-Differences (DiD) analyses to estimate intervention effects over time.
3. Use Propensity Score Matching (PSM), weighting, and related approaches to improve comparability between treatment and control groups.
4. Evaluate methodological assumptions, including parallel trends, overlap, confounding, and treatment selection.
5. Apply regression-based causal models, fixed-effects models, and appropriate econometric techniques.
6. Conduct robustness checks, sensitivity analyses, placebo tests, and alternative model specifications.
7. Clearly communicate causal assumptions, limitations, and uncertainty.
C. MEASUREMENT STRATEGY AND BUSINESS IMPACT
1. Develop measurement frameworks to quantify the incremental impact of business initiatives.
2. Translate business objectives into measurable outcomes and appropriate analytical designs.
3. Evaluate measurement methodologies based on business questions, available data, and operational constraints.
4. Identify confounding factors, selection effects, and sources of measurement bias.
5. Develop reusable analytical frameworks, standardized templates, and measurement documentation.
6. Present findings and strategic recommendations to senior stakeholders.
7. Distinguish causal conclusions from correlations and associations.
D. COLLABORATION AND TECHNICAL LEADERSHIP
1. Partner with product, marketing, business, engineering, and analytics teams to define measurement requirements.
2. Review experimental designs, analytical methodologies, model assumptions, and statistical interpretations.
3. Mentor junior data scientists and promote best practices in experimentation and causal inference.
4. Support scalable measurement pipelines and automated reporting processes.
5. Communicate complex statistical concepts to non-technical stakeholders.
REQUIRED QUALIFICATIONS
1. 5–7 years of relevant experience in data science, experimentation, measurement science, econometrics, quantitative research, or causal inference.
2. Master's degree or equivalent quantitative background in Statistics, Economics, Econometrics, Mathematics, Data Science, or a related discipline. A PhD is an advantage.
3. Strong foundation in statistical inference, probability theory, regression analysis, hypothesis testing, and experimental design.
4. Hands-on experience designing and analyzing A/B tests and controlled experiments.
5. Practical experience with statistical power analysis, confidence intervals, sample size estimation, and MDE calculations.
6. Strong knowledge of quasi-experimental methods, particularly Difference-in-Differences (DiD) and Propensity Score Matching (PSM).
7. Understanding of causal inference, treatment effects, confounding, selection bias, and identification assumptions.
8. Proficiency in Python or R and strong SQL skills.
9. Experience with statistical libraries such as statsmodels, SciPy, scikit-learn, or equivalent R packages.
10. Ability to select appropriate statistical methods based on business questions and data constraints.
11. Strong communication and stakeholder management skills.
PREFERRED QUALIFICATIONS
1. Experience with inverse probability weighting, doubly robust estimation, instrumental variables, regression discontinuity, or synthetic control methods.
2. Experience with longitudinal, panel, customer-level, marketing, product, or operational datasets.
3. Familiarity with heterogeneous treatment effects and uplift modeling.
4. Experience with marketing effectiveness, incrementality testing, attribution, or product experimentation.
5. Knowledge of Bayesian experimentation or sequential testing methods.
6. Experience developing reusable measurement frameworks and automated experiment reporting.
7. Experience mentoring junior analysts or reviewing complex analytical projects.
EXPECTED OUTCOMES
1. Deliver statistically sound experiments and measurement methodologies.
2. Produce credible estimates of incremental business impact.
3. Develop robust causal analyses with clearly documented assumptions and limitations.
4. Improve experimentation practices, measurement consistency, and decision quality.
5. Deliver actionable business recommendations supported by statistical evidence.
Pay: ₹1,040,000.00 - ₹2,470,000.00 per year
Application Question(s):
- Notice Period?
- Current CTC?
Work Location: In person
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