• Write production code in Python. • Design, launch, and analyze experiments to optimize ad campaigns. • Build reporting and analytics tools for the Data Science Team's customers. • Translate into tvScientific's Data Science Product.

• Production-Grade Modeling: Design, develop, and maintain reproducible machine learning models for production environments. • Behavioral Forecasting: Build advanced models to predict user engagement patterns, including retention and activity frequency. • Optimization & Causal Inference: Develop Uplift models to measure the incremental impact of business interventions. • Recommendation Systems: Improve and scale next-best-action (NBA) engines to enhance personalized user experiences. • Experimental Design: Lead the statistical design and analysis of A/B testing to validate model performance and business hypotheses.

• Design and maintain credit risk and fraud detection models • Perform feature engineering on large structured financial datasets • Train, validate, and optimise machine learning models for production use • Monitor model performance and implement continuous improvements • Collaborate with ML engineers on deployment, tracking, and lifecycle management • Integrate model outputs into LangChain and LangGraph orchestration pipelines • Ensure model explainability, robustness, and regulatory compliance • Support documentation and governance requirements in a regulated environment

• Develop machine learning models for player performance analysis and optimisation • Analyse time series and event based sports data at scale • Engineer features from tracking, workload, and contextual datasets • Validate, monitor, and continuously improve models in production environments • Collaborate with sports analysts and domain experts to refine analytical use cases • Expose model outputs to analytical dashboards and downstream systems • Integrate model outputs into LangChain and LangGraph based reasoning workflows • Ensure model outputs are interpretable, reliable, and decision ready