Sr. Data Scientist

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 114,000 colleagues serve people in more than 160 countries.

     

JOB DESCRIPTION:

WORKING AT ABBOTT

At Abbott, you can do work that matters, grow, and learn, care for yourself and family, be your true self and live a full life. You’ll also have access to:

• Career development with an international company where you can grow the career you dream of.
• Free medical coverage for employees* via the Health Investment Plan (HIP) PPO
• An excellent retirement savings plan with high employer contribution
• Tuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.
• A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
• A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.

The Opportunity

This position works out of our Pleasanton, California location in the Heart Failure Division. In Abbott’s Heart Failure (HF) business, we’re developing solutions to diagnose, monitor and manage heart failure, allowing people to restore their health and get on with their lives.

As a Senior Data Scientist, you will play a crucial role in shaping our data-driven strategies and contributing to the success of our Heart Failure initiatives. You will be responsible for designing, developing, and deploying advanced data science solutions to extract valuable insights from large and complex datasets. As a key member of our team, you must be capable of exhibiting a sense of inquisitiveness and a creative mind to uncover opportunities that will directly contribute to enhancing the quality of patient care and the development of innovative medical devices and therapy solutions.

What You’ll Work On

Data Wrangling and Statistical Analysis

• Collaborate with cross-functional teams to define project objectives.
• Utilize large datasets from various sources, including medical devices and phenotype data.
• Data structuring, cleaning, and statical analysis.
• Conduct exploratory data analysis to understand trends and patterns.

Machine Learning:

• Develop and refine features for predictive modeling, ensuring that relevant clinical variables are incorporated effectively.
• Build and validate predictive models for clinical outcomes, patient risk assessments, and other relevant applications, using appropriate machine learning algorithms.
• Evaluate and optimize models for accuracy and performance.

Clinical Insights:

• Collaborate closely with cross-functional teams, including medical experts, clinical research scientists, and engineers to identify clinically significant insights and patterns within the data.
• Understanding of physiological systems and therapies in the cardiovascular space is a plus

Data Visualization:

• Create informative data visualizations and dashboards to communicate findings and insights to non-technical stakeholders.

Data Governance:

• Implement data governance best practices to ensure data quality, integrity, and compliance with relevant regulations, such as HIPAA.

Research and Innovation:

• Stay updated with the latest developments in data science and machine learning to continually improve data analysis methodologies and techniques.
• Identify opportunities for innovation and propose data-driven solutions.

Collaboration:

• Collaborate with external partners, academic institutions, and healthcare providers to access additional data sources and research opportunities.

Documentation:

Maintain comprehensive documentation of data analysis processes, methodologies, and findings.

Required Qualifications

• Bachelor’s Degree in Data Science, Computer Science, Statistics, or a related field plus 5 years of experience.