Business Analyst – Scientific AI, with Data Statistics & Programming

Business Analyst – Scientific AI, with Data Statistics & Programming – REQ

Full-Time / Direct-Hire  |  Foster City , CA USA

We''re hiring a Scientific Business Analyst with hands-on AI exposure to help translate complex clinical and R&D problems into well-structured, AI-ready requirements. You''ll sit at the intersection of life sciences and AI product delivery — partnering with Product Managers, AI Engineers, Data Scientists, and clinical stakeholders to shape use cases, define success metrics, and drive requirements through to release. This role is ideal for someone who can independently own the full BA lifecycle for AI-enabled scientific products, from discovery through UAT.

•        Solid understanding of the clinical development lifecycle and pharmaceutical R&D processes

•        8+ years of experience within pharmaceutical, biotech, or clinical research organizations

•        8+ years of business analysis and requirements-gathering experience, ideally on data or AI-enabled products

•        Demonstrated ability to translate ambiguous scientific or business problems into structured, testable requirements

•        4+ years defining KPIs, success metrics, and measurable outcomes for product or process initiatives

•        Experience owning UAT planning, execution, and stakeholder sign-off

•        Working knowledge of clinical development concepts (trial phases, regulatory milestones, translational science)

•        Bachelor''s or Master''s degree in Life Sciences, Health Sciences, Biotechnology, Biomedical Sciences, Pharmacy, or a related field

•        Comfortable working independently with minimal oversight in a fast-moving, ambiguous environment

•        This role carries the full scientific business analysis and adds hands-on statistical and programming capability.

•        Translate business and scientific needs into AI-ready requirements — covering input data expectations, model outputs (predictions, insights, recommendations), and user interaction patterns (workflows, prompts, dashboards)

•        Own UAT end to end — plan, scenarios, cycles, defect triage, summary report.

•        Apply working knowledge of clinical research and drug development to ensure requirements and acceptance criteria reflect true scientific intent

•        Document functional and AI-specific requirements, including data inputs, model outputs, and user workflows, with clear acceptance criteria

•        Lead requirements refinement — current/future-state analysis, prioritization, feasibility discussions, and stakeholder alignment

•        Act as the primary bridge between business needs and technical feasibility across scientific and engineering teams

•        Own UAT planning and execution end-to-end, partnering with QA/testing teams to ensure requirements are testable

•        Work within Agile/Scrum delivery using Jira or Azure DevOps

•        Python for analysis: pandas and numpy, fluently, matplotlib or seaborn, scikit-learn and stats models well enough to prototype and to read someone else''s model code.

•        Advanced SQL — window functions, CTEs — for independent investigation across large datasets. R where the biostatistics teams work in it. Jupyter and Git as normal practice

•        User stories and acceptance criteria maintained in Jira/ADO; formal URS/FRS documentation where required

•        Current- and future-state artifacts: process flows, data flows, and impact summaries

•        Data specifications, source-to-target mappings, and AI input/output definitions, produced in partnership with Data and AI teams

•        UAT plans, test scenarios, and test cases aligned to approved requirements

•        UAT execution: test cycle coordination, defect logging, retesting, and summary reporting

•        Regular status reporting covering progress, risks, issues, and dependencies

•        Understanding of AI/ML concepts and Generative AI solutions

•        Exposure to prompt engineering, AI agents, or LLM-based workflows

•        Experience working with AI products, analytics platforms, or intelligent automation initiatives

•        Basic Python for exploratory data analysis

•        Basic SQL for data validation and analysis

•        GxP / Computer System Validation (CSV) exposure

•        You understand business priorities, clinical workflows, and the AI product roadmap well enough to drive independently

•        Stakeholders trust you to turn ambiguous scientific problems into clear, well-scoped AI requirements

•        Your BA artifacts (user stories, URS/FRS, process flows, acceptance criteria) are consistently release-ready

•        AI input/output definitions and success metrics are clearly established for prioritized initiatives

•        UAT cycles you lead result in clean, on-time releases with minimal requirement rework

•        You become a trusted partner bridging scientific stakeholders and AI engineering teams

Work at the intersection of AI and life sciences, helping shape the next generation of drug discovery and clinical development tools. Partner daily with scientists, clinicians, AI engineers, and product leaders on Generative AI and agentic AI initiatives with real-world healthcare impact. Gain hands-on exposure to modern AI and cloud-based data platforms, in a collaborative environment built around continuous learning and growth.