Data Scientist responsible for analyzing complex data sets and developing methods to create actionable insights. Collaborate with engineering teams to improve data quality and deliver business value.
About the role
- Senior Data Scientist delivering end-to-end AI solutions at Seagate. Leading complex data science projects and partnering with research institutions for innovation.
Responsibilities
- Lead and deliver end‑to‑end data science and AI initiatives, from problem definition and modeling through production deployment and operationalization.
- Design, develop, and deploy advanced machine learning and Generative AI solutions to address complex business and engineering challenges.
- Translate business and technical problems into scalable, data‑driven solutions with measurable impact.
- Partner closely with cross‑functional stakeholders and subject‑matter experts to align AI solutions with business objectives.
- Communicate insights, trade‑offs, and recommendations effectively to both technical and non‑technical audiences through clear narratives, reports, and visualizations.
- Collaborate with external research institutions on exploratory and forward‑looking data science initiatives.
- Stay current with emerging trends, tools, and techniques in data science, machine learning, and Generative AI, and help shape technical direction for the team.
Requirements
- Master’s or Ph.D. in Data Science, Computer Science, Statistics, or a related quantitative field.
- 8+ years of experience in data science, machine learning, and/or Generative AI, with a strong track record of delivering real‑world solutions.
- Hands‑on development and deployment of production ML and Generative AI systems, including full lifecycle ownership from design to rollout.
- Proven delivery of end‑to‑end GenAI solutions, encompassing model selection, system architecture, implementation, testing, and productionization.
- Strong experience building Retrieval‑Augmented Generation (RAG) systems, including embeddings, vector databases, retrieval pipelines, and LLM integration.
- Expertise in large‑scale data analysis, feature engineering, and statistical modeling on complex datasets.
- Proficiency in Python and SQL, with experience building robust data processing and ML pipeline components.
- Experience mentoring junior data scientists and providing technical leadership on complex projects.
Benefits
- None specified
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