Data Scientist 2

Responsibilities

• 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.
• 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.

Qualifications

• 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.