英语高级R&D IT需求分析（数据科学）

Main Responsibilities
Stakeholder discovery and migration mapping: Lead discovery with users to inventory current analyses, environments, data dependencies, and controls; define migration plans from legacy Scientific Computing Platform to new platform with clear success criteria.
Regulatory alignment: Interpret and operationalize requirements and controls relevant to the data request process; embed these into platform configuration, workflows, documentation, and training.
Requirements and backlog management: Elicit and document functional, data, security, and non-functional requirements; write user stories and acceptance criteria; manage the migration and platform enablement backlog with traceability.
Platform enablement: Partner with platform engineers to configure projects, workspaces, hardware tiers, environments, model registry, reproducibility settings, and access controls; standardize templates/patterns for common R&D use cases.
Data platform integration: Coordinate data access patterns from data sources to platform; specify data products, schemas, lineage, quality checks, and governance metadata; support secure data movement and caching strategies.
Analytics and workflow transition: Translate scientific pipelines (Python/R, notebooks, PK/PD models, statistics workflows) into compatible jobs/schedules; support PoCs and productionization with attention to performance, cost, and reproducibility.
Process and control design: Define end-to-end processes for request intake, environment provisioning, dependency management, validation/qualification where applicable, and release/change control aligned to internal SOPs and relevant regulations.
Adoption, training, and change: Create quick-start guides, curated environments, and role-based training; run enablement sessions; collect feedback and iterate; serve as Level 2/3 functional support for use cases.
Vendor/product coordination: Liaise with internal&external platform/product teams; track issues and roadmap items; contribute to release planning and risk management.
Security, privacy, and compliance: Ensure solutions meet China and global data privacy/cybersecurity expectations, company SOPs, and GxP/GCP where in scope; maintain auditable documentation and access reviews.
Identify business improvement opportunities, Design To-Be business process with new solution
Required qualifications
Education: Bachelor’s or higher in Data Science, Bioinformatics, Biology, Pharmacology, Biomedical Engineering, Statistics, Computer Science, or related field.
Domain knowledge: Formal training and/or hands-on experience in data science or bioinformatics, or biological domains (e.g., translational research, PK/PD, biostatistics, clinical data).
Platform/tools experience: Practical experience with data science platform (project/workspace configuration, environment management, job scheduling, model registry).
Data and analytics literacy: Strong understanding of Python/R, notebooks, package/dependency management, reproducibility, experiment tracking, and CI/CD concepts; familiarity with ELT/ETL, APIs, and data quality practices.
Compliance mindset: Experience translating regulatory/policy requirements into process and technical controls; ability to author clear documentation and acceptance tests for compliance evidence.
Communication: Fluent in English and Chinese (written and spoken); adept at translating between scientific, technical, and compliance stakeholders.
BA/Agile skills: Proficient in user story writing, process modeling, backlog/prioritization, and acceptance testing; experienced in Agile/Scrum delivery.
Stakeholder leadership: Proven ability to lead cross-functional workshops, resolve ambiguities, and drive decisions under time constraints.
Preferred qualifications
Migration experience: Track record migrating analytics workloads to data science platforms (e.g., Domino, Databricks, SageMaker, Vertex AI), including environment parity, dependency mapping, and change management.
Data standards and R&D context: Familiarity with pharma R&D data types and standards (e.g., SDTM/ADaM, SEND, assay/omics, imaging, real-world data) and with PK/PD or statistical workflows.
Ecosystem tools: Exposure to Airflow/Prefect, Git/GitHub, MLflow, containerization, Spark; experience with role-based access control, secrets management, and audit logging.
Regulatory familiarity: Understanding of data security concepts, GxP/GCP expectations for analytics where applicable, and China data/cybersecurity regulations.
Certifications: CBAP/PMI-PBA, Agile (PSPO/CSPO/SAFe), or data platform certifications (Domino, Snowflake).