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 patternsComparative effectiveness analysesDrug and device utilizationNatural 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.
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