Data Science and Business Analytics Manager

Abbott Laboratories
Alameda, CA, United States
29 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$145,000.0 - $185,000.0
Working hours
Regular working hours
Job source

Tech stack

Health Informatics Information Engineering Data Infrastructure Extract Transform Load (ETL) Data Visualization Data Warehousing R (Programming Language) Machine Learning Power BI Tableau (Software) Tools for Reporting

Job description

Abbott Laboratories seeks a Data Science and Business Analytics Manager to lead advanced analytics supporting key decisions in biotechnology and pharmaceuticals. You will manage a small team of data scientists and analysts, design end-to-end analytics solutions, and translate complex data into clear insights for business and clinical stakeholders. Partner with R&D, commercial, and operations teams to optimize product performance, forecast demand, and improve patient and customer outcomes. Promote a culture of experimentation, mentor talent, and champion data-driven decision-making in a highly regulated environment., * Lead and mentor a team of data scientists and business analysts delivering high-impact analytics projects.

  • Design and implement end-to-end analytical solutions from data acquisition through modeling and deployment.
  • Collaborate with R&D, commercial, and operations teams to define analytical questions and translate findings into action.
  • Develop predictive models and statistical analyses to support product strategy, demand forecasting, and operational efficiency.
  • Create clear dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders.
  • Ensure data quality, governance, and compliance with healthcare and pharmaceutical regulations.
  • Drive a culture of experimentation and hypothesis-driven decision-making.
  • Partner with IT and data engineering to improve data infrastructure and analytics tools.

Requirements

  • Statistical modeling
  • Machine learning
  • SQLPython or R programming
  • Data visualization (Tableau/Power BI)
  • ETL and data warehousing
  • A/B testing and experimental design
  • Forecasting and predictive analytics
  • Business intelligence and reporting
  • Pharmaceutical or healthcare analytics

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