Role & responsibilities:

- Model Risk Management (MRM) & Regulatory Compliance: Author comprehensive model validation documentation (SR 11-7 standards) and defend model design, mathematical assumptions, and limitations to internal Model Risk Management teams, audit, and regulatory bodies.
- Exploratory Data Analysis: Conduct thorough EDA on large, complex datasets to uncover patterns, anomalies, and relationships that inform feature engineering and model selection.
- Feature Engineering: Build and refine feature sets using domain knowledge, statistical methods, and automated feature selection techniques to maximize model performance and interpretability.
- Model Explainability and Governance: Apply SHAP, LIME, or similar explainability frameworks to ensure model outputs can be understood by business stakeholders, internal audit, and regulators.
- MLOps and Productionization: Work with Data Engineers to operationalize models through repeatable pipelines, versioning, monitoring, and drift detection frameworks.
- Stakeholder Communication: Present model findings, performance metrics, and business implications to both technical teams and senior leadership, translating complex results into explicit narratives.
- Research and Innovation: Stay current with advances in ML and AI research, evaluating new techniques and frameworks for applicability to banking and financial services use cases.
- Generative AI and LLM Implementation: Evaluate, fine-tune, and deploy Large Language Models (LLMs) and Generative AI frameworks (e.g., Retrieval-Augmented Generation / RAG)



to automate complex textual analysis, document processing, or unstructured data workflows within banking domains.
- Business Impact and Financial Attribution: Establish clear metrics to link model performance directly to financial and operational business outcomes (e.g., credit risk reduction, fraud loss prevention, or operational efficiency gains), ensuring data science initiatives deliver measurable ROI.
- Technical Mentorship and Code Governance: Lead code reviews, enforce rigorous testing frameworks (unit testing for ML pipelines), and mentor mid-level and junior data scientists on robust experimental design and scalable Python/R engineering practices

Preferred candidate profile:

- Experience: 7+ years in an applied Data Science role, with at least 3 years deploying models to production in a regulated industry.
- Technical Stack: Expert proficiency in Python (scikit-learn, TensorFlow, PyTorch, XGBoost) or R for model development and experimentation.
- Statistical Foundation: Strong grounding in statistics including hypothesis testing, probability distributions, regression analysis, Bayesian inference, and experimental design.
- MLOps Tooling: Experience with MLflow, Databricks, or Kubeflow for experiment tracking, model registry, and deployment pipelines.
- Financial Domain: Familiarity with financial risk models, credit scoring, fraud detection, anomaly detection, or AML use cases is strongly preferred.
- Explainability: Hands-on experience with SHAP, LIME, or model governance frameworks relevant to regulated industries.

📌 Senior Data Scientist (Noida)
🏢 NLB Services
📍 Noida