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.