Principal Data Scientist

Veracyte•San Diego, CA

3h•$188,000 - $210,000•Remote

About The Position

At Veracyte, we offer exciting career opportunities for those interested in joining a pioneering team that is committed to transforming cancer care for patients across the globe. Working at Veracyte enables our employees to not only make a meaningful impact on the lives of patients, but to also learn and grow within a purpose driven environment. This is what we call the Veracyte way – it’s about how we work together, guided by our values, to give clinicians the insights they need to help patients make life-changing decisions. Our Values: We Seek A Better Way: We pursue bold ideas, embrace complexity, and keep pushing forward. We Make It Happen: We act with urgency, deliver with excellence, and always find a way. We Are Stronger Together: We engage with empathy, align around what's best for Veracyte, and celebrate as one team. We Care Deeply: We show up with integrity, kindness, and respect for one another. The Position: We are seeking a talented and experienced Principal Data Scientist with proven expertise in developing oncology-based digital pathology AI models (DPAI) to join our Veracyte’s Data Science team. This position offers a unique opportunity to work with skilled professionals to drive Veracyte’s DPAI-based research program. Successful candidate is expected to lead algorithm development of new diagnostic products based on digital pathology by leveraging one of the world’s largest multi-modal oncology databases.

Requirements

PhD in Data Science, Machine Learning, Applied Math or equivalent field.
8+ years of experience of data/applied scientist role or equivalent in disease-related field, (cancer preferred).
Expert in Python or equivalent language for AI/ML development in the context of computer vision / DPAI (this includes data manipulation and preparation).
Proficient in statistical analysis, especially in survival modelling and hypothesis testing (i.e., multivariate regression modelling with interaction effects).
Experience working in cloud computing environments (AWS preferred).
Demonstrated proficiency in summarizing and communicating findings from data, including attention to detail when sharing findings.
Demonstrated ability to clearly explain AI/ML concepts to both experts and non-experts in text and figures via formal presentations and academic writing.
Ability to work effectively in a fast-paced and collaborative environment.
Eagerness to learn new technologies and adapt to evolving requirements.
Direct technical leadership and employee management experience, including the ability to break down complex problems into actionable and delegable projects.
Passionate about data, independently eager to learn, and possesses strong analytical, problem-solving, and story-telling skills.

Nice To Haves

Experience serving as technical leader or manager.
Proficiency with documentation and submission in regulated diagnostic environments (LDT or IVD).
Experience working with real world clinical data and evidence.

Responsibilities

Develop DPAI models using WSI data to predict clinical outcomes and pathological/morphologic features ensuring their biological explainability, including adapting open-source state-of-the-art AI foundation models to Veracyte’s and collaborator
Identify the correct data sources for developing DPAI models and lead team efforts to obtain annotation or labels for ML development.
Analyze complex data/ML problems and break them into actionable sub projects or tasks. Coordinate the delegation of tasks to junior team members as technical lead, track progress and ensure correctness of the deliverables.
Collaborate with both internal and external partners to understand the clinical and business requirements for given products and tailor projects and algorithms accordingly.
Partner closely with bioinformatics, statistical, and medical experts to document projects, including writing and generating analyses and visualizations for publication in peer-reviewed journals.
Lead collaboration within and across teams with the ability to manage project execution and completion.
Coordinate with both internal and external medical experts to identify critical research questions and datasets that build evidence for the utility of our models.