Data Scientist

Core Requirements

• 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.

Key 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 clear 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.