• 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

• Develop portfolio optimisation and customer lifetime value models • Analyse customer behaviour, profitability drivers, and retention patterns • Apply statistical modelling, machine learning, and optimisation techniques • Translate strategic business questions into structured analytical solutions • Prepare and validate datasets for modelling and scenario analysis • Collaborate with finance, pricing, and strategy stakeholders • Expose analytical outputs to downstream systems and LangChain / LangGraph pipelines • Ensure model robustness, consistency, and governance alignment

• Review and evaluate clinical data generated from the imaging portion of sponsor clinical trials across various therapeutic areas. • Perform all data management deliverables for assigned studies, ensuring timelines and quality standards are met. • Support study setup activities, including eCRF/TF design, database structure, and specifications, obtaining and providing necessary approvals. • Understand study implementation and data collection requirements and translate these into robust data management plans. • Collaborate with project teams to develop and maintain study-specific edit check criteria. • Define, run, and review edit checks, identify discrepant data, and drive timely data query resolution. • Coordinate, document, and oversee the receipt and processing of electronic or ancillary data from external sources (e.g., central labs, CROs, sponsors). • Maintain organized, complete, and up-to-date study documentation in accordance with internal and regulatory standards. • Track outstanding data issues, follow up with stakeholders, and ensure discrepancies are resolved and documented. • Ensure accuracy and consistency in the manipulation, review, and validation of clinical data, adhering to relevant regulatory guidelines. • Adhere to Clinical Data Management procedures, policies, and SOPs, and contribute to the review and improvement of departmental standards and processes. • Collaborate with internal teams to resolve operational issues, share knowledge, and support others in achieving project goals. • Participate in company-sponsored training to maintain and enhance technical and industry knowledge.