Data Scientist - Domain AI & Model Engineering

• Build predictive, prescriptive and generative intelligence that improves business decisions across IOH priority domains, while creating reusable models and analytical assets for downstream decisioning and agentic workflows.
• Translate business problems into analytical hypotheses, experiments and production-ready models across customer, commercial, network and enterprise use cases.
• Develop model outputs beyond a single score, including key drivers, confidence, eligibility, expected value and recommended action where relevant.
• Partner with Data Engineering and AI Platform/MLOps to ensure data readiness, deployment, monitoring, retraining and production reliability.
• Partner with AI BI + Agentic Factory so model outputs can be consumed safely in business workflows, decision products and automated interventions.
• Monitor model performance, drift, adoption and realized business impact while applying responsible AI, fairness and explainability principles.

Strategic Planning

• Translate priority business problems into reusable model and experimentation roadmaps.
• Identify where predictive, forecasting, optimization, recommendation or GenAI approaches create meaningful business advantage.
• Define analytical success metrics that connect model quality to business outcomes and value.

Operational

• Execute the end-to-end Data Science lifecycle: data exploration, feature engineering, modelling, validation, experimentation, deployment support and monitoring.
• Design outputs that downstream decisioning and agentic systems can consume safely, including scores, drivers, confidence, eligibility and action recommendations.
• Continuously monitor drift, model performance, adoption and realized impact; recommend retraining or redesign where needed.

Cross-Functional Delivery

• Work closely with Data Engineering, AI Platform/MLOps, AI BI + Agentic Factory and business owners to industrialize models.
• Contribute reusable methods, features, code and evaluation patterns that reduce one-off Data Science work.
• Support knowledge sharing and raise the technical bar across the Data Science community.

External Relation

• Evaluate relevant external AI/ML tools, platforms, datasets and partners where they materially improve delivery.
• Represent IOH in selected technical / industry discussions and stay current on applied ML and GenAI developments.
• Avoid unnecessary dependency on external vendors by building reusable internal capability.

Qualification:

Minimum bachelor's degree in STEM, Statistics, Mathematics, Computer Science, Engineering, Economics or another quantitatively rigorous discipline.

Experience:

Minimum 4-8 years in applied Data Science, Machine Learning or quantitative analytics, with evidence of solving real business problems and moving models beyond experimentation into operational use.

Skills:

• Strong Python and SQL capability, including feature engineering, reproducible analysis and large-scale data manipulation.
• Strong applied machine learning and statistics fundamentals including classification, regression, forecasting, experimentation and model evaluation.
• Experience with causal inference, optimization, recommendation or next-best-action approaches is highly valued.
• Experience deploying or supporting models on cloud ML platforms such as Vertex AI, including monitoring, retraining and model lifecycle practices.
• Working knowledge of BigQuery / modern cloud data environments and collaboration with data engineering teams.
• GenAI literacy including foundation models, prompt engineering, RAG, structured outputs and model-to-agent consumption patterns.
• Strong business judgment: able to connect model performance to intervention economics, adoption and measurable business value.
• Able to communicate analytical trade-offs, limitations, explainability and responsible-AI considerations clearly to technical and business stakeholders.