Data Scientist

Job Description: Lead / Senior Machine Learning Engineer (Predictive Analytics) Location: Mumbai (On\-site/Hybrid) Experience: 5 – 10 Years Notice Period: Immediate Joiners Preferred Domain: Financial Services (Banking, Insurance, or Asset Management) **Role Overview** As a Lead Machine Learning Engineer, you will bridge the gap between complex financial data and actionable business strategy. You won't just build models; you will design the predictive engines that power credit scoring, fraud detection, churn prediction, or portfolio optimization. We need a hands\-on expert who understands the "why" behind the math and the "how" of production\-grade deployment. **Core Responsibilities** · End\-to\-End ML Development: Lead the design, development, and deployment of predictive models (Regression, Time\-series, Random Forests, XGBoost, etc.) tailored for FS use cases. · FS\-Specific Analytics: Apply machine learning to solve domain\-specific problems such as Credit Risk scoring, Customer Lifetime Value (CLV), Attrition Modeling, or Claims Propensity. · Feature Engineering: Architect robust feature pipelines from disparate financial sources (transactional logs, CRM data, market feeds). · Model Governance: Ensure all models meet FS regulatory standards, focusing on interpretability (SHAP/LIME) and bias mitigation. · Strategy \& Mentorship: Guide junior data scientists and collaborate with stakeholders to translate business problems into technical roadmaps. · Productionalization: Work with MLOps to deploy models into high\-availability environments, ensuring scalability and performance monitoring. **Technical Requirements** · Advanced Analytics: 5\-10 years of experience in Predictive Analytics with a proven track record in the Financial Services sector. · Tech Stack: \* Languages: Expert\-level Python or R. o ML Frameworks: Scikit\-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. o Data Handling: Advanced SQL and experience with Spark/PySpark for large\-scale financial datasets. · Cloud \& MLOps: Experience with AWS SageMaker, Azure ML, or Google Vertex AI. · Mathematics: Strong foundation in statistics, probability, and linear algebra as applied to financial forecasting. **Preferred Qualifications** · Experience dealing with imbalanced datasets (common in fraud and default prediction). · Understanding of financial regulations (e.g., IFRS 9, Basel III) and their impact on data modeling. · Master’s or PhD in Statistics, Mathematics, Computer Science, or Economics. What We Offer · A leadership role in a fast\-paced FS analytics hub in Mumbai. · Direct impact on revenue\-generating products and risk\-mitigation strategies. · Exposure to cutting\-edge AI/ML tooling and cloud infrastructure.