Staff Data Scientist

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Job description

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.

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 student debt program and 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

Staff Data Scientist works as integral part of a collaborative data and analytics team and responsible for analyzing real-world data to generate insights and develop machine learning models that drive the development and optimization of devices in Abbott Diabetes Care. The role requires drawing insights, and presenting results in a cohesive, intuitive, and simple manner to the functional stakeholders utilizing technologies to collect, clean, analyze, predict, and effectively communicate insights. Key functional stakeholders include research & development, clinical, medical, regulatory and market access teams.

What You'll Work On

Analyze large real-world datasets including device data, electronic health records (EHR), claims data, labs, and patient registries.

Support the design and execution of RWE studies including but not limited to:Treatment optimization and understanding treatment patterns

Comparative effectiveness analyses

Drug and device utilization

Natural history and burden of disease

Healthcare resource utilization

Analyze data, draw insights, and present results in a cohesive, intuitive, and simple manner to functional stakeholder.

Utilize technologies to collect, clean, analyze, predict, and effectively communicate insights such as model logic and restrictions.

Conduct advanced statistical analysis to determine trends and significant data relationships.

Develop machine learning models to apply test data algorithms to future data.

Validate models/analytical techniques and develop algorithms to execute analytical functions.

Collaborate with clinical, medical, regulatory, and market access teams to integrate RWE into product development and lifecycle management.

Work closely with the functional stakeholders to understand the domain and iteratively refine analyses.

Provide learning and educational pathways for team members.

Provides input into developing departmental and site processes and procedures.

Guide and otherwise contribute to technical teams in development, deployment and application of applied analytics, predictive analytics, prescriptive analytics, etc.

Independently manages and consults in multiple complex projects working with stakeholders to define business questions, requirements, timelines, objectives, and success criteria to address needs.

Experience in creating and advanced statistics such as: regression, time-series forecasting, clustering, decision trees, exploratory data analysis methodology, simulation, scenario analysis, modeling, optimization, unstructured data analysis, and neural networks.

Researches and adapts existing open-source algorithms when possible and develops novel techniques when needed.

Stay current with industry trends, regulatory requirements, and best practices in RWE and data science.

Required Qualifications

Bachelor's Degree in Life or Physical Science, Bioengineering, Biomedical Engineering

4-6 years' work-related experience with degree or sufficient transferable experience to demonstrate functional equivalence to a degree.

Advanced Experience with programming scripts such as Python, Java, Scala, C++ in Linux/Unix, and R.

Experience in applying data analysis techniques to a large set of data using big data systems such as Hadoop, Spark, MongoDB, or similar software.

Advanced analytics knowledge and application in the field of: Statistics, Mathematical programming.

Business acumen and experience with operational or strategic systems.

Preferred Qualifications

Bachelor's Degree in Biostatistics, Epidemiology or closely related discipline is preferred.

Prior experience in real world evidence, health economics, or outcomes research, in the medical device/pharma/CRO industry is preferred.

Proficiency in statistical software (e.g. R, Python) and familiarity with Databricks is preferred.

Experience with machine learning and artificial intelligence applications in healthcare is preferred.

Familiarity with regulatory guidelines and requirements for medical devices is preferred.

Publications in peer-reviewed journals or presentations at scientific conferences is preferred.

Participants who complete a short wellness assessment qualify for FREE coverage in our HIP PPO medical plan. Free coverage applies in the next calendar year.

Learn more about our health and wellness benefits, which provide the security to help you and your family live full lives:

Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.

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The base pay for this position is $(phone number removed) - $(phone number removed). In specific locations, the pay range may vary from the range posted.

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