Data Science Manager(Causal)

We are looking for a highly skilled Data Scientist with strong expertise in Causal Modelling and Classical Machine Learning to work on advanced analytics initiatives. The primary focus of this role will be on Automated Insights Generation , where causal inference techniques will be applied to identify key drivers and relationships within complex datasets. The ideal candidate should have hands-on experience in both DAG-based and non-DAG causal algorithms , strong ML fundamentals, and experience in deploying machine learning models into production environments .

The role will also have exposure to future Generative AI initiatives , making familiarity with GenAI concepts an added advantage. Key Responsibilities Design and implement causal inference models using both DAG-based and non-DAG-based algorithms to generate actionable insights. Work on Automated Insights Generation systems that identify cause-effect relationships from large-scale datasets.

Develop, train, validate, and optimize classical machine learning models for predictive and analytical use cases. Build end-to-end machine learning pipelines , including: Data preprocessing Feature engineering Model training and validation Model deployment Implement model deployment frameworks and enable real-time or batch model scoring on live data . Collaborate with cross-functional teams including data engineers, product teams, and client stakeholders .

Participate in client-facing discussions and technical interviews to demonstrate expertise and project capability. Ensure scalability, performance, and maintainability of ML solutions. Stay updated with advancements in causal AI, machine learning, and generative AI technologies .

Mandatory Skills & Qualifications Strong expertise in Causal Modelling / Causal Inference . Hands-on experience with DAG (Directed Acyclic Graph) based algorithms and non-DAG causal modelling techniques . Strong foundation in Classical Machine Learning algorithms such as: Regression Classification Clustering Ensemble methods Experience in end-to-end machine learning lifecycle , including: Model development Deployment Monitoring Model scoring on production/live data Strong programming skills in Python and experience with ML libraries (e.g., Scikit-learn, Pandas, NumPy).

Experience working with large datasets and advanced analytics workflows . Ability to clear client technical interviews and work in a collaborative client environment. Good-to-Have Skills Exposure to Generative AI concepts and tools .

Experience with LLMs, prompt engineering, or AI-based automation solutions . Knowledge of MLOps tools and frameworks . Experience working with cloud platforms such as AWS, Azure, or GCP.