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# Data Science and Business Analytics Manager

- **Company:** Abbott Laboratories
- **Location:** Alameda, CA, United States
- **Salary:** $145,000.0 - $185,000.0
- **Contract:** Permanent contract
- **Skills:** 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
- **Published:** October 5, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=3005092772-2

## About the Role

* 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

## 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.