Senior Data Scientist

Vor 2 Tagen

Zug, Schweiz TN Switzerland Vollzeit

AbbVie’s Operations Quality Data Analytics group is continuously innovating to empower the AbbVie network with superior Quality, Product, and Process analytics that enable data-driven insights and decision-making at every organizational level. This Senior Data Scientist role will have wide-ranging analytics experience to work within a diverse Operations business. The Senior Data Scientist will build and oversee the building of predictive/prescriptive models using statistical and machine-learning methodologies. The Senior Data Scientist will also ensure end-to-end delivery of assigned data science applications, studies, and POCs through oversight of project teams and key technology partnerships.

Consulting and collaboration skill sets are key to success in this role. Additional experience of pharmaceutical regulations, knowledge of cGMPs, awareness of international regulatory trends, and knowledge of OPEX fundamentals is preferred. This demanding role will have a highly visible impact to senior leaders across Operations.

Responsibilities
  • Operate cross-functionally in a highly collaborative manner with fellow data scientists, senior functional leaders, and business partners.
  • Consult with business users in the analysis of requirements and recommend solutions which anticipate the future impact of changing business needs.
  • Develop advanced data analytical models and techniques from supervised and unsupervised machine learning, statistical analysis, deep learning, predictive modeling, and Gen AI.
  • Build relationships with stakeholders and collaborate with other statistical and data science teams in AbbVie while mentoring and coaching team members.
  • Collaborate with academic partners on research projects and POCs.
  • Maintain and execute project roadmaps in alignment with functional and business partners at multi-year, annual, and quarterly timescales.
  • Devise and execute effective communication plans with key stakeholders.
  • Demonstrate thought leadership through proposals, white papers, architectural inputs, and planning for current and future state needs.
  • Quickly assess new opportunities (AI/ML techniques, technology, etc.) and present these within the current and future state of requirements, risk, and cost-effectiveness.
  • Perform requirements discovery, determine feasibility of various approaches, devise experimental approaches to test assumptions, and devise solutions which incorporate non-functional requirements such as scalability, interpretability, and maintainability.
Qualifications
  • Master’s or PhD in a related science or quantitative field.
  • Minimum of 2 years of experience in a regulated environment.
  • Experience in full life cycle and evolution of complex data science applications including Agile Project management processes.
  • Self-learner able to quickly become effective in new tools, concepts, and academic findings relevant to stakeholder needs.
  • Experience in Quality, Manufacturing, or Central Operations context in a large pharmaceutical environment including cGMP systems and processes.
  • Awareness or experience in key industry trends in related areas such as Industry 4.0, Quality Risk Management, or Knowledge Management in pharmaceutical manufacturing operations.
Tools and Skills
  • Expert knowledge of statistical and machine learning methods, with expertise in modeling and business analytics.
  • Data Science tools like Knime, Dataiku, AWS SageMaker; statistical languages, and packages, e.g., R, Python, Julia, SAS; data visualization tools and techniques, e.g., Tableau, ggplot, matplotlib, R/Shiny, Spotfire; data manipulation tools such as SQL, Spark, PySpark and Hive; cloud environments like Azure and AWS; deep learning tools such as Tensorflow, Keras, and PyTorch.
  • Expertise in designing and developing processes and systems to consolidate and analyze data. Considerations include variety (structured, unstructured), volume (includes big data), data quality (cleaning and validation of the data), and velocity (batch, stream).
  • Expertise in processes for missing data analysis, data quality, master data management, and good preparation practices for modeling.
  • Expertise in storytelling with data and communicating complex topics to audiences of all levels.
  • Able to drive internal learning through journal clubs, internal data science education sessions and continuously deepen the team knowledge in data science practice and research.
  • Able to communicate effectively to an executive level audience.
Additional Information

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives, serving our community and embracing diversity and inclusion. It is AbbVie’s policy to employ qualified persons of the greatest ability without discrimination against any employee or applicant for employment because of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information, gender identity or expression, sexual orientation, marital status, status as a protected veteran, or any other legally protected group status.

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