Data Scientist 2

• Identify and frame AI opportunities across Technical Development, Manufacturing, Quality, and Supply Chain
• translate ambiguous problems into tractable use cases with measurable outcomes
• Maintain TOPS Data Science Portfolio of Projects
• Participate in Portfolio prioritization, planning, solution design, development, and deployment
• Lead Projects from start to finish by closely working with stakeholders, leadership and project team
• Author business case, design, development and project implementation documents
• Advance the Integrated Technical Data Strategy by defining roadmaps, value hypotheses, and success metrics that strengthen process robustness, speed, and cost/value realization
• Acquire and prepare multi-source technical data (e.g., MES, LIMS, QMS, ELN, SAP, PI), ensuring quality, lineage, and context for AI development at scale
• Engineer domain-aware features and reusable data assets that accelerate experimentation for manufacturing, quality, and supply analytics
• Build and validate ML/AI models for use cases such as process monitoring, anomaly/root-cause analysis, yield and cycle-time optimization, and intelligent document processing
• Develop GenAI solutions (e.g., RAG for SOPs/reports, Semantic search, Q&A assistants over technical data, workflow copilots) using approved enterprise platforms
• Operationalize models (MLOps) with reproducible pipelines by closely working with Data Engineering team—data ingestion, training, evaluation, versioning, deployment—and monitor drift, performance, and data quality for continuous improvement
• Collaborate with IT/Engineering to ensure scalable, secure, and supportable AI services aligned to TOPS environments and platform standards
• Drive data visualization and decision support with clear narratives and dashboards that communicate model insights to engineers, operators, quality leads, and executives
• Champion data integrity and documentation (e.g., model cards, validation records) consistent with TOPS quality expectations and regulated biotech practices
• Educate and enable partners through demos, playbooks, and training that raise data/AI literacy and adoption across TOPS functions
• Quantify and report value realization (e.g., cost avoidance, OEE improvements, cycle-time reduction, quality signal detection) and maintain a transparent backlog of AI initiatives
• Promote “build-first” evaluations against internal platforms before third-party tools when requirements are met internally with better agility and cost efficiency
• Contribute to TOPS AI standards (feature stores, evaluation frameworks, prompt/agent guidelines) and mentor peers to strengthen the data science community of practice
• Stay current on AI advances (foundation models, time-series, causal inference, simulation/digital twins) and assess applicability to manufacturing, quality, and supply use cases

• Master’s (minimum) in Data Science, Computer Science, Statistics, or related field
• 5+ years of hands-on experience delivering Data/AI solutions in an industry setting
• Advanced SQL and Python for data wrangling, feature engineering, modeling, and automation
• Experience developing Python based web applications using frameworks such as Dash, Flask, Streamlit
• Familiarity with HTML/CSS and TS frameworks (React) is a plus
• Strong experience working with Databases (Postgres, SQL Server) and Data Platforms (Azure Databricks)
• Proven record of successful end-to-end data analysis project management: from problem and requirements definition to data validation and results presentation
• Proficiency with one or more enterprise Business Intelligence technologies (Power BI, Tableau, Spotfire)
• Solid understanding of Data modelling principles and design patterns
• Proven experience building and operationalizing GenAI pipelines (Chunking, RAG, Vector index) on Databricks (Delta, Unity Catalog, MLflow, Jobs/Workflows, Spark, Lakeflow)
• Working knowledge of Microsoft Azure (storage, compute, identity/governance, Azure OpenAI)
• High level understanding of data engineering pipelines and data quality practices
• Experience extracting/structuring data from unstructured sources (SOPs, reports, PDFs, ELN entries) using NLP or GenAI
• Demonstrated experience in biotech/biopharma operations and partnering with SMEs across technical development, manufacturing, quality, or supply
• Familiarity with Computer System Validation (CSV) documentation practices in regulated environments
• Strong communication skills supporting collaboration across Technical Development, Manufacturing, Quality, and Supply Chain

Familiarity with HTML/CSS and TS frameworks (React) is a plus

• company-sponsored medical, dental, vision, and life insurance plans
• paid time off
• discretionary annual bonus
• discretionary stock-based long-term incentives