Managing Data Scientist (Chemistry, Materials Controls – Acceleration)

Role Summary

Managing Data Scientist (Chemistry, Materials Controls – Acceleration) responsible for leading a multi-level data science delivery team to build predictive/prescriptive models supporting CMC acceleration from R&D into commercial production. Role involves end-to-end delivery of data science applications, overseeing mid-level and junior data scientists and data analysts, and acting as a thought leader to the group strategy and education activities. Approximately 50% client-facing and 50% project involvement.

Responsibilities

• Operate cross-functionally with OBI data scientists, functional leaders, and business partners.
• Manage a diverse talent pool to deliver project-based data science and transformative projects.
• Consult with business users to analyze requirements and recommend solutions anticipating future business needs.
• Drive technology transfer of data science methods into functional areas and expand the footprint of data science within manufacturing and CMCM processes.
• Develop advanced data analytical models using supervised/unsupervised learning, deep learning, and predictive modeling.
• Build relationships with stakeholders and mentor team members; collaborate with other data science teams.
• Collaborate with academic partners and AbbVie Innovation Center on research projects and POCs.
• Assess new opportunities (AI/ML techniques, technology) and present within requirements, risk, and cost context; drive GenAI initiatives.

Qualifications

• Required: Undergraduate with 7 years of experience, master's with 6 years, or PhD with 2 years in a related quantitative field in a non-academic setting (data science/statistics/predictive analytics). Preference for quantitative/engineering undergraduates (chemistry/chemical engineering, mathematics, etc.).
• Experience in full lifecycle and evolution of complex data science applications including Agile project management.
• Consulting skills and experience: maintaining roadmaps, materials, communication plans, and thought leadership; mentoring mid-career/junior resources; self-learning ability.
• Ability to communicate to executive-level audiences.

Preferred

• Experience in manufacturing and science/technology tech transfer of pharma products to commercialization, including GxP systems.
• Experience in technology transfer and innovation practices via consulting or corporate roles.
• Experience in MLOps/SLC in pharmaceutical operations.
• Experience in strategy or boutique consulting with direct stakeholder interaction.

Skills

• Expert knowledge of statistical and machine learning methods; strong modeling and business analytics.
• Consulting skills as a thought partner for senior stakeholders.
• Proficiency with Dataiku, AWS SageMaker, R, Python, Julia, SAS; data visualization (Tableau, ggplot, matplotlib, R/Shiny, Qlik); SQL, Spark, PySpark, Hive; cloud (Azure, AWS); deep learning (TensorFlow, Keras, PyTorch).
• Advanced AI development approaches (e.g., RAG, semantic data layering) for AI-augmented human-in-the-loop applications.
• Experience consolidating and analyzing diverse data (structured and unstructured), ensuring data quality and handling big data, batch and streaming.
• Data quality, master data management, and preprocessing for modeling.
• Storytelling with data and communicating complex topics to varied audiences.
• Ability to lead internal learning and education in data science practices.
• Effective communication to executive audiences.

Education

• Not explicitly listed beyond degrees in Qualifications; advanced degrees in quantitative fields encouraged, with preference for chemistry/chemical engineering, mathematics, or related industrial/engineering disciplines.