Designing, developing, validating, and deploying data science and ai solutions
Hands-on analytical, modeling, and engineering work
Design, build, test, and iterate on data science, machine learning, and generative ai (genai) solutions
The Data Science Pod Member is an individual contributor responsible for designing, developing, validating, and deploying data science and AI solutions as part of a multidisciplinary pod
Job Summary
The Data Science Pod Member is an individual contributor responsible for designing, developing, validating, and deploying data science and AI solutions as part of a multidisciplinary pod.
Pod Members collaborate closely with the Data Science Pod Lead, AI Product Owner, AI Architects, and engineering partners to deliver measurable value while adhering to enterprise standards and Responsible AI principles.
This role focuses on hands-on analytical, modeling, and engineering work, translating business and product objectives into high‑quality technical deliverables.
Matching Summary
The Data Science Pod Member is an individual contributor responsible for designing, developing, validating, and deploying data science and AI solutions as part of a multidisciplinary pod.
Skills & Requirements
Must-have
designing, developing, validating, and deploying data science and AI solutions
hands-on analytical, modeling, and engineering work
design, build, test, and iterate on data science, machine learning, and Generative AI (GenAI) solutions
Perform data exploration, feature engineering, model training, evaluation, and validation
Implement solutions that are scalable, maintainable, and aligned with enterprise architecture
Contribute production-ready code, notebooks, pipelines, and model artifacts
Nice-to-have
collaboration with Data Science Pod Lead
partner with AI Product Owners
communicate analytical findings to diverse audiences
stay current with advances in data science, ML, and Generative AI techniques
contribute ideas to improve tools, processes, and reusable assets
Key Requirements
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
Proficiency in relevant programming languages and tools (e.g., Python, SQL, notebooks, ML frameworks)
Knowledge of the software development life cycle
Knowledge of AI and Generative AI concepts, risks, and opportunities