Responsibilities
- Elevate Patient perspective in Drug Discovery and Development: Ensure Valo’s real-world data (RWD) platform is optimally positioned for teams to extract actionable insights, directly influence drug discovery and development programs, while supporting platform scalability and expansion.
- Champion the Valo RWD user experience: responsible for integration, adoption, and uptake of Valo’s electronic health record data and RWD platform across Valo computational teams. Develop rapid and robust user onboarding to data assets embedded in engineered pipelines and tools developed for specific projects.
- Improve patient representation AI in RWD workflows (eg, feature engineering, data curation, fit-for-purpose tooling). Build from common data models that enable fluid integration across data assets.
- Lead Patient Data Asset Platform: Develop and execute Valo’s platform strategy, ensuring alignment with overall organizational goals, while working closely with software and infrastructure engineers, epidemiology and data science leaders, and drug discovery researchers.
- Own RWD tooling roadmap to unlock RWD capabilities for maximal impact. Collaborate with teams to build scalable real-world evidence products that accelerate insight generation, for existing partnerships, and generate new entry points for RWD in business development.
- Partner with epidemiology and data science leaders to execute continuous evaluation of RWD data assets. Develop automated systems to validate/QC data processing for existing and prospective RWD vendors. Systematically evolve patient feature mapping and standardization across RWD sources.
- Lead high-performing team: Responsible for developing team roadmap, mentoring, and career development of a multidisciplinary clinical informatics, data science, and data engineering group. Foster a culture of excellence dedicated to scientific rigor and continuous learning while contributing to Valo’s broader goal of being recognized externally for cultural excellence.
Team
Structure: Multidisciplinary team of data scientists, data engineers, and informaticists; reports to leadership and collaborates with epidemiology, machine learning, product engineering, and business innovation leaders.
