Principal Scientist, Real World Evidence # 3325

Responsibilities

• Develop harmonized data models/ data dictionaries that guide the collection, processing, linkage, integration and analyses of healthcare RWD from disparate sources (EHRs, claims, cancer & death registries, PROs, etc.). 
• Evaluate new and existing data sources for fitness & feasibility to unlock new opportunities.
• Ensure data quality, privacy and security through pre-defined data quality review documentation, and pre-established data governance policies/procedures.
• Explore development of new approaches to RWD capture through GRAIL-sponsored studies, registries, clinical surveillance and/or other RWE activities.
• Collaborate with cross functional stakeholders to develop and implement shared data processing strategies, state-of-the-art statistical methods and machine learning algorithms (e.g. NLP) to enable RWE generation, interpretation & visualization from global datasets, in an interpretable, reliable fashion. 
• Collaborate with Biostatistics group in developing statistical methods used in a RWE study setting and causal inference for RWE (e.g., propensity score matching/weighting/subclassification, external control arms, count data regressions, survival analysis).
• Remain up-to-date on the latest advancements in observational research methods and their applications; healthcare data sources; Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles; data privacy; etc. 
• As a member of a cross-functional team, GRAIL’s Principal Scientist, RWE will build and maintain cross-functional relationships with Medical Affairs, Clinical Development, Regulatory, and business teams; and collaborate with the team effectively to ensure the implementation of rigorous methods and delivery of scientifically sound results, in support of regulatory submissions, publications and/or other uses. 
• Drive the evaluation of technical strengths/weaknesses, work to minimize the limitations of RWD, and influence the decision-making process.
• Support the development of appropriate sections within study protocols, statistical analysis plans, data management plans, data quality review documentation, etc. 
• Collaborate with external partners (e.g., key opinion leaders, academic & community health systems and networks, CROs, vendors, etc.) on the design and execution of RWD/E studies.
• As needed, support management of data acquisition and/or analysis vendors and external partners.
• Contribute to scientific publications, conference presentations, and internal knowledge sharing activities to disseminate research findings and promote scientific excellence within the organization.
• Mentor junior members of the team.

Preferred Qualifications

• Ideal candidate will have a Ph.D. with 12+ years relevant experience or M.S. with 15+ years relevant experience in data science, applied mathematics, biostatistics, epidemiology or similar. Experience in Oncology or diagnostic devices is strongly preferred. 
• Expertise in R or Python is required. Direct experience in SQL is preferred.
• Solid understanding of statistics and machine learning techniques; and integrating ML/NLP and other automated technologies into data processing design. 
• Deep knowledge of the journey of real world data→ real world evidence, such as challenges in data access, de-identification, missingness, data wrangling/curation/transformation/linkage/interoperability, etc.; and hands-on experience in solving these challenges. 
• Demonstrated experience with 1) developing harmonized data models for RWD implemented for prospective RWD collection from disparate sources ; 2) private and public real-world healthcare data sources (non-interventional studies, electronic medical records, administrative healthcare claims, disease registries, national surveys etc.); 3) modalities for data linkage and interoperability (e.g. tokenization, etc.); 4) observational data analysis & comparative effectiveness methods; 5) data processing and quality assurance approaches for both structured and unstructured data.
• Demonstrated experience working with Clinical Data Management, Statistical Programming, Biostatistics, and Software teams.
• Experience in the design, execution and reporting of regulatory-grade real-world data studies in the cancer screening, diagnostic, medical device, or pharmaceutical industry.
• Good knowledge of international regulatory and other guidelines, not limited to ICH, GCP, HIPAA, NICE, and other RWE guidelines.
• Good communication and collaborations skills (including interpersonal skills to contribute effectively in cross-functional team settings, ability to influence others without authority, ability to build strong collaborative relationships with scientific and non-scientific partners)
• Good strategic ability (including problem-solving and critical thinking skills, ability to drive strategies, agility that extends beyond RWD expertise)
• Motivated by success and passionate about working and achieving higher results (demonstrates interest and ability to learn new things, takes initiative, welcomes problems as challenges; finds solutions to technical problems)
• Clear, positive, and proactive communicator in verbal and written communications
• Willingness to be hands-on and solve technical problems