Senior Data Scientist_Causal(Immediate Joiner)

Job description

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.