Data Scientist II, HEOR

Key Skills

Aartificial intelligence, Agile methodologies, C++, Data Analysis, Data Engineering, Data Modeling, Data Visualization, Database, Deep Learning, Design, Java Programming, Leadership, Machine learning techniques, Management, Mathematics, MATLAB Programming, Python Programming, R Programming, SAS, SPSS Statistics, SQL

Job Description

The Data Scientist II, Health Economics and Outcomes Research (HEOR) provides strategic development and use of cutting-edge mathematical and economic models to enhance understanding of colorectal cancer and other malignancies, to quantify, and to communicate the value of Exact Sciences’ molecular diagnostics technologies to patients, providers, payers, and policy makers. This role provides strategic and technical leadership using model-based scientific and business insights across all phases of product development. This role will apply analytical methods and statistical analysis to diverse sources of data and share results in varying formats requiring an interdisciplinary background demonstrating knowledge of domain expertise, including data manipulation, data visualization, and data engineering, and statistical methods. The Data Scientist II, HEOR position has oversight of pre-market and modeling efforts within the medical affairs team.

This position is remote.

Essential Duties

Include but are not limited to the following:

• Perform large-scale experimentation and build data-driven models to answer business questions.
• Create hypotheses and experiments to identify hidden relationships and construct new analytics methods.
• Develop simulation models and conduct cost-effectiveness analysis.
• Apply machine learning/deep learning/artificial intelligence techniques.
• Use advanced analytics methods to extract value from business data.
• Articulate a vision and roadmap for the utilization of data as a valued corporate asset.
• Visualize information and develop reports on results of data analysis.
• Ability to develop and complete innovative data projects in a fast-paced environment.
• Influence product teams through presentation of data-based recommendations.
• Implement new tools to make data analysis more efficient.
• Willing and able to learn new technologies on the job.
• Apply strong communication skills, including verbal, writing, and presentation.
• Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
• Support and comply with the company’s Quality Management System policies and procedures.
• Regular and reliable attendance.
• Ability to communicate complex analytical results to technical and non-technical audiences.
• Ability to work on a mobile device, tablet, or in front of a computer screen and/or perform typing for approximately 90% of a typical working day.

Minimum Qualifications

• Master’s degree in mathematical epidemiology, applied mathematics, operations research, or other quantitatively-oriented discipline.
• 4+ years of experience in the design and development of modeling approaches.
• 3+ years of experience supporting highly visible statistical projects of data within a fast-paced environment.
• Fluency in Python, C++, and/or R programming.
• Demonstrated ability to perform the Essential Duties of the position with or without accommodation. Authorization to work in the United States without sponsorship.

Preferred Qualifications

• PhD in a quantitatively-oriented discipline; such as mathematical epidemiology, applied mathematics, or operations research.
• 6+ years of experience in the design and development of modeling approaches.
• Working knowledge with statistical methodologies and tools (e.g., R, SAS, SPSS, MatLab, etc.), mathematical optimization, simulation, and time-series analysis. 
• Working knowledge with machine learning (to solve complex business problems), predictive modeling, leveraging both structured and unstructured data source, relational databases, and SQL.
• Working experience of cost-effectiveness analysis.
• Working knowledge of statistics and modeling techniques.
• Strong record of research publications in peer-reviewed journals.