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.

Masters 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.