Data Scientist- Causal Modeling

1 week ago

Bengaluru, Karnataka, India Tredence Full time

About the Role

We are looking for a highly skilled Data Scientist /Sr Data Scientist / Lead Data Scientist with expertise in Causal Modeling to join our analytics team. The ideal candidate will have strong statistical and econometric foundations, hands-on experience in designing and implementing causal inference methods, and the ability to derive business insights that influence strategic decisions.

Key Responsibilities

  • Design and implement causal inference frameworks to measure the true impact of business initiatives, marketing campaigns, and product interventions.
  • Apply methods such as propensity score matching, instrumental variables, difference-in-differences, regression discontinuity, uplift modeling, and causal graphs.
  • Build and validate robust statistical and machine learning models to support experimentation (A/B testing, quasi-experiments).
  • Partner with cross-functional teams (product, marketing, operations, data engineering) to define problems, frame hypotheses, and deliver actionable insights.
  • Develop scalable analytical pipelines and dashboards for causal impact monitoring.
  • Stay up to date with advances in econometrics, Bayesian methods, and causal ML techniques.

Required Skills & Qualifications

  • Masters or PhD in Statistics, Econometrics, Computer Science, Mathematics, Economics, or a related field.
  • 310 years of experience as a Data Scientist / Quantitative Researcher / Econometrician.
  • Strong knowledge of causal inference methods and experimental design.
  • Proficiency in Python / R / SQL for data manipulation, statistical modeling, and visualization.
  • Experience with ML frameworks (scikit-learn, PyTorch, TensorFlow) is a plus.
  • Strong analytical mindset with the ability to translate complex results into clear business recommendations.

Good to Have

  • Experience in forecasting, recommendation systems, or optimization.
  • Exposure to big data tools (Spark, Snowflake, Databricks, etc.).
  • Publications or applied research in causal inference or related fields.

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