Sports Injury Data Scientist, RWE Agile Analytics

Summary:

The vision of our Real World Evidence teams is to pioneer and lead with scientific and technical expertise, the generation and dissemination of high-quality Real World Evidence that make a true difference to patient lives, globally. We have unique opportunities to define the way our clients develop and apply Real World Evidence for healthcare decision making, so we are looking for the best talent to join us in this effort.

Purpose:

IQVIA Real World Evidence Agile Analytics is hiring for a Sports Injury Data Scientist position. The team is looking for someone who takes initiative, thrives in a fast-paced data-driven environment, and wants to utilize their knowledge of data science and data warehousing as it relates to observational real-world research. The ideal candidate will be comfortable with using technology in various settings (i.e., tech savvy), have experience performing data management, curation, and analytics utilizing real-world data, and have a solid understanding of data warehousing in a healthcare setting.

As a member of the Agile Analytics team, you are a contributing member of a small multi-disciplinary scientific team focused on understanding client needs, expectations, and producing accurate data-driven results that impact real-time decision-making in the sports setting. You will lead and participate in data curation and development of analytic datasets using complex and fit-for-purpose data sources, mapping of new data sources, identifying proper linkages between data sources, and driving data quality requirements and implementation. You will also have the opportunity to conduct ad hoc analytics, communicate analytic results to a variety of audiences and contribute to scientific presentations in an agile analytic environment. Research topics focus on injury prediction and prevention, surveillance and monitoring, program evaluation, protocol evolution, and intervention effectiveness.

Responsibilities:

• Performs exploratory analysis of new data sources to assess data quality, feasibility of use for research, and applicability based on project research needs.
• Cleans and integrates data from a variety of primary and secondary sources, including electronic health records, diagnostic results, genomics, survey, and sports injury/performance data (e.g., player tracking data, game performance data, surface and environmental data, etc.).
• Leads the creation of fit-for-purpose analytic datasets through both automated and supervised data curation processes, driven by internal and external requirements gathering.
• Leads and may oversee comprehensive data management tasks, data quality control processes, and data cleaning to support scientific-focused project and task needs.
• Manages and participates in cross-functional communications with various internal and external technology partners, with an eye towards ensuring team needs are consistently and continually met.
• Works with data integration programmers and database administrators to manage their development, implementation, and maintenance of internal SQL databases and related technology platforms. As part of a larger data team, leads and oversees: requirements gathering, data mapping activities, identifying proper cross-source data linkages, data validation, and other ongoing database enhancements.
• Conducts ad hoc and scheduled analytics using SAS or R as part of a large analytic and cross-functional team in a fast-paced environment.
• Prepares reports or presentations that summarize these analyses, interprets the results, and provides conclusions and recommendations in non-technical language.
• Builds, manages, and oversees automated reports and data listings.
• Brings solution-oriented approach to project challenges and enjoys complex problems and working through unknowns.
• May participate in client-facing project meetings as a recognized member of the project team, and directly interfaces with the client to coordinate deliverables and develop requirements.
• Mentors and guides junior team members, including providing direction and oversight on data-focused and analytical tasks.
• Participates in the evaluation and integration of potential new analytic technologies and applications.
• Develops data visualizations and/or analytic insights.

Required Knowledge, skills, and abilities:

• Strong SQL programming skills along with an understanding of database design and data collection methodology, including the underlying relational and dimensional data models.
• Working knowledge of general-purpose programming applications (Python, Perl, Ruby, C++, Java, etc.); familiarity with an object-oriented programming language is a plus.
• Moderate to advanced knowledge of SAS required (including Base SAS, SAS/STAT, SAS/GRAPH, SAS/ACCESS, Enterprise Guide); additionally, knowledge of R is a plus.
• Proficient in Microsoft Excel (including PivotTables), Word, PowerPoint, and other related skills.
• Familiarity with basic statistical analysis techniques (e.g., regression/modelling, hypothesis testing, categorical data analysis) and data visualization is a plus.
• Excellent critical thinking and analytical skills, strong attention to detail, and ability to effectively prioritize and manage multiple tasks.
• Excellent communication skills including written and verbal; ability to present ideas clearly and concisely.
• Ability to establish and maintain effective working relationships with co-workers, managers, and clients in a global and matrixed environment.
• Ability to work independently and as a contributing team member.

Required Education and Experience:

• Bachelor's Degree with 6-8 years relevant experience.
• Master's Degree in Computer Science, Applied Mathematics, Operations Research, Information Systems, Statistics, Data Science, or related field, with 3-5 years relevant experience.

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