Data Scientist- LLM/GenAI

Key Responsibilities

• Perform data preparation, feature engineering, and exploratory analysis on transactional, behavioural, third-party, and alternative data.
• Develop predictive models for risk scoring, underwriting, propensity, early-warning signals, forecasting, and workflow prioritisation.
• Define target variables, observation windows, and performance windows carefully, accounting for time lags, maturity, and data leakage.
• Validate models using out-of-time testing and assess performance on discrimination, calibration, stability, and business impact.
• Help turn model outputs into practical decision rules, thresholds, segments, and action strategies.
• Support model deployment, monitoring for drift and performance, and recommend recalibration or retraining when needed.
• Partner with engineering to keep training and production features consistent and to support reliable pipelines.
• Maintain clear documentation, reproducibility, and explainability so models can support governance and review.
• Contribute to AI/LLM initiatives (e.g., virtual CFO or agent-style workflows) through feature design, evaluation, or integration as needed.

Required Skills and Experience

• Strong proficiency in Python and SQL, with experience working on large datasets.
• Practical experience with logistic regression and tree-based models (XGBoost, LightGBM, CatBoost, or similar), and knowing when simpler models are preferable.
• Solid grounding in statistics and experimentation, including sampling and selection bias, class imbalance, and careful treatment of leakage.
• Experience evaluating models using AUC, KS, Gini, precision/recall, calibration, and stability measures such as PSI.
• Familiarity with explainability techniques such as SHAP and ability to explain model behaviour to non-technical stakeholders.
• Experience contributing to model deployment and monitoring in production environments.
• Bachelors or masters degree in statistics, mathematics, computer science, engineering, economics, or a related quantitative field.

Nice to Have

• Exposure to deploying or evaluating AI/LLM inference workflows (latency, cost, reliability, output quality).
• Familiarity with LLM tool/function calling, external API integration, or multi-step agent-style workflows.
• Understanding of supervised fine-tuning, instruction tuning, or preference optimisation approaches.

Experience putting evaluation, guardrails, observability, or human review in place for sensitive AI