Job Description

The Senior Analyst, Global Data Science & Analytics will work with a team of data scientists and data engineers to deliver data science solutions The Sr Analyst will leverage data analysis and life sciences skills to support various projects for internal and external stakeholders, for example deriving data-driven insights to guide clinical trial design, performing real-world data studies to monitor performance of medical devices and providing real-time intelligence on market share These projects use machine learning, artificial intelligence, statistical modeling, data mining, and visualization techniques to provide analytics solutions to a wide range of challenging business problems Knowledge, Experience and Education: Education: Bachelors degree a. bachelors degree in an engineering or science field (example: biomedical engineering, biotechnology, bioinformatics, biostatistics or similar) with a demonstrable focus on programming and data analysis b. Years of experience, both overall and any industry-specific experience needed: 1-2 years of work experience required, preferably working on life sciences focused projects Familiarity with at least one programming language (Python, PySpark, SQL, R) Knowledge of at least one database software preferable but not required (Databricks/SQL) Other qualifications/certifications. 1-year of experience in medical device/pharmaceutical industry or experience working with health insurance claims preferred Able to work both independently and as a team member Experience coordinating and prioritizing for multiple tasks Good organizational and time management skills Able to communicate effectively, both orally and in writing Excellent attention to detail and accuracy Major Accountabilities 1. Conduct statistical analysis of real-world datasets based on the scientific hypothesis 2. Perform programming in Python, SQL and/or R to analyze insurance claims and other medical data. 3. Provide study support by reading and summarizing relevant background material, clinical study papers and publications. 4. Lead activities performed to keep an ML model running smoothly, adaptively, and effectively post-deployment. 5. Assist in interpretation and presentation of results through figures/tables/reports, including necessary explanation of data and analysis. 6. Work as part of a team to understand and deliver solutions for complex data and business problems. Principle Challenges Follows diverse procedures and problems require consideration of many different approaches. Solutions are selected from alternatives using past experience Solution: candidate must work on projects for multiple verticals, which will involve building upon existing code and scripts to adapt and create new code for different projects. Candidate must use life sciences background and contribute to feasibility assessment of new scientific projects. In addition, candidate might be required to support rerunning of data models for existing dashboards and troubleshooting issues in programming

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