Director RWE - Data Scientist (OMOP/OHDSI)

Key Skills

Analytical and Problem solving, Effective communication skills, HIPAA, Machine learning techniques, Python Programming, R Programming, SQL

Job Description

Gilead Sciences, Inc. is a research-based biopharmaceutical company founded in 1987. Together we deliver life-saving therapies to patients in need. With the commitment and drive you bring to the workplace every day, you will be part of a team that is changing the world and helping millions of people live healthier, more fulfilling lives. Our worldwide staff is a close community where you can see the tangible results of your contributions, where every individual matters, and everyone has a chance to enhance their skills through ongoing development.

As part of our rapidly growing science-driven organization, colleagues at Gilead are revolutionizing healthcare by bringing urgently needed medicines to patients in the areas of virology, including HIV/AIDS, haematology and oncology, inflammation and respiratory diseases. We are proud to have some of the most talented colleagues from across the research, healthcare, pharmaceutical, biotechnology & business sectors, working together and supporting each other to help make a real difference to the lives of patients.

This position is office based at our Stockley Park or Cambridge sites in the UK

We are seeking a skilled and motivated senior Real World Evidence (RWE) Data Scientist working in the area of Observational Health Data Sciences and Informatics (OHDSI) to join our dynamic team. As a RWE Data Scientist (OMOP/OHDSI), you will play a crucial role in designing, implementing, and maintaining healthcare data solutions using the OHDSI framework.

This position offers an exciting opportunity to contribute to advancements in health informatics and research.

Responsibilities:

• Collaborate with researchers and data scientists to understand project requirements and translate them into OHDSI-compatible solutions.
• Work with databases, ensuring data integrity and optimization for OHDSI-related queries and analyses.
• Perform data analyses in OHDSI-related tools like ATLAS.
• Customize and extend OHDSI tools and applications to meet specific project needs.
• Collaborate with cross-functional teams to troubleshoot and resolve technical issues related to OHDSI implementations.
• Stay informed about OHDSI community updates, best practices, and emerging trends in observational health data research.
• Contribute to the development and documentation of data standards and conventions within the OHDSI community.

Qualifications:

• Bachelor’s degree with substantial coursework in Computer Science, Medical Informatics, Bioinformatics, or related fields. Advanced degree preferred.
• Proven experience in programming languages commonly used in the OHDSI community, such as SQL, R, and Python.
• Understanding of Machine Learning Concepts.
• Solid Understanding of Clinical Terminology such as SNOMED, ICD9/10, CPT, HCPCS, READ
• Solid understanding of healthcare data, particularly electronic health records (EHR) and insurance claims, and experience working with healthcare data standards.
• Proven experience with the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and OHDSI related tools, such as ATLAS, ATHENA, and ACHILLES.
• Strong problem-solving skills and the ability to work collaboratively in a team environment. Excellent communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders. Experience developing and maintaining documentation for RWE studies and insight generation.

Preferred Skills:

• Knowledge of clinical informatics and healthcare research methodologies.
• Experience with data quality assessment and data validation techniques.
• Familiarity with healthcare privacy and security regulations, such as HIPAA.

If you are passionate about leveraging your programming skills to contribute to innovative health data research, we invite you to apply and be a key player in advancing the field of observational health data sciences.