AI Foundations Pod Member

Data Science & Solution Development

• Design, build, test, and iterate on data science, machine learning, and Generative AI (GenAI) solutions aligned to pod objectives, including LLM‑based systems, retrieval‑augmented generation (RAG), agents, and multimodal use cases.
• Perform data exploration, feature engineering, model training, evaluation, and validation, including GenAI‑specific evaluation (e.g., groundedness, hallucination risk, latency, cost, and quality metrics).
• Implement solutions that are scalable, maintainable, and aligned with enterprise architecture, data, and engineering standards, with explicit consideration for GenAI safety, security, and Responsible AI controls (prompt management, guardrails, data provenance, and access controls).
• Contribute production‑ready code, notebooks, pipelines, and model artifacts, including prompts, system instructions, evaluation harnesses, and GenAI configuration assets.

Delivery & Execution

• Execute assigned work items to meet sprint and increment commitments aligned to the product roadmap.
• Balance experimentation with delivery, supporting the transition from proof‑of‑concept to production.
• Identify and communicate technical risks, assumptions, data limitations, and trade‑offs to the Pod Lead.
• Support operational readiness through testing, documentation, and handover activities.

Collaboration & Ways of Working

• Work closely with the Data Science Pod Lead to align technical work with pod‑level direction and priorities.
• Partner with AI Product Owners to understand business problems, success metrics, and value hypotheses.
• Collaborate with platform, data engineering, MLOps, and software engineering teams.
• Communicate analytical findings, model behavior, and recommendations to both technical and non‑technical stakeholders.

Quality, Governance & Responsible AI

• Ensure models and analytics meet quality, performance, security, reliability, and compliance standards.
• Apply Responsible AI principles throughout the solution lifecycle.
• Produce and maintain appropriate technical documentation, experiments, and traceability artifacts.

Continuous Improvement & Innovation

• Stay current with advances in data science, ML, and Generative AI techniques.
• Contribute ideas to improve tools, processes, and reusable assets across the data science practice.
• Participate in communities of practice, knowledge sharing, and peer reviews

Degree Requirement

Bachelor’s degree in engineering, computer science, data science, mathematics, statistics, or a related field (or equivalent practical experience). Advanced degree (Master’s or PhD) in artificial intelligence, machine learning, engineering, mathematics, physics, or a closely related field is considered an advantage.

Skill Descriptors

• Data Science & ML Foundations :Hands‑on experience applying statistical analysis, machine learning, and/or Generative AI techniques to real‑world problems.
• Programming & Tooling: Proficiency in relevant programming languages and tools (e.g., Python, SQL, notebooks, ML frameworks); ability to write, test, debug, and maintain production‑quality code.
• Data‑Informed Problem Solving: Ability to analyze data, experiments, and model performance metrics to generate insights and guide technical decisions.
• Agile Delivery: Experience working in Agile teams, contributing to sprint planning, estimation, and iterative delivery.
• Communication & Collaboration: Clear verbal and written communication skills, with the ability to explain technical concepts and analytical results to diverse audiences..

Level Working Knowledge

• Identifies and documents specific problems and potential analytical or modeling approaches.
• Examines problems from multiple stakeholder perspectives.
• Develops and evaluates alternative techniques for assessing accuracy, relevance, and performance.
• Uses appropriate fact finding techniques, diagnostics, and experimentation

Software Development Life Cycle

• Knowledge of the software development life cycle
• Ability to work within a structured methodology for delivering and maintaining data science and AI solutions, including development, testing, deployment and support

Artificial Intelligence

• Knowledge of AI and Generative AI concepts, risks, and opportunities; ability to govern and