Causal Machine Learning Scientist.
This mandate is run by SYMPHONI HR, a Mumbai-based executive search firm, est. 2003. Applications are email-verified and reach the search team running the role — confidentially, always.
About the organisation.
Our client is a leading global professional services firm specializing in accounting and financial advisory. They are committed to leveraging advanced data science and artificial intelligence to drive strategic decision-making for their diverse portfolio of international clients. The organization fosters a culture of innovation, analytical rigor, and professional growth.
About the role.
We are seeking a Causal Machine Learning Scientist to join a high-impact team in Bengaluru. In this hybrid role, you will design and implement sophisticated causal inference models to measure the impact of business interventions and optimize decision-making processes. You will bridge the gap between complex observational data and actionable business insights using cutting-edge statistical and machine learning techniques.
What you will do.
- Design and analyze end-to-end controlled experiments including A/B tests and weblabs.
- Perform power and sample-size analysis to ensure robust experimental outcomes.
- Develop causal graphs to identify confounders and mediators for valid effect estimation.
- Implement observational estimators such as Double/Debiased ML, Propensity Score Matching, and Inverse Propensity Weighting.
- Build and maintain scalable causal pipelines from raw data to defensible effect estimates.
- Translate complex causal findings into clear, actionable recommendations for non-technical stakeholders.
- Integrate LLM agents into the causal analysis loop to assist with hypothesis generation and estimator selection.
- Collaborate with cross-functional teams to deploy solutions within an AWS-based data environment.
What you bring.
- 5–7 years of professional experience in data science or machine learning.
- Proficiency in Python and SQL for data manipulation and model development.
- Deep expertise in causal inference methodologies including synthetic control and difference-in-differences.
- Hands-on experience with causal libraries such as DoWhy, EconML, and CausalPy.
- Strong understanding of AWS data and analytics services including Redshift, S3, Lambda, and ECS.
- Proven ability to communicate uncertainty and assumptions effectively to business leaders.
- Experience working in a hybrid environment based in Bengaluru.
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