Machine Learning Researcher
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
Role Overview:
The Machine Learning Researcher bridges the gap between our data assets and our customers' needs in our healthcare vertical. They play a key role in ensuring our datasets are well-matched to the AI models our customers are building and well-understood by those customers. This role requires both healthcare data expertise, extensive experience with statistical analysis, and some customer collaboration.
Key Responsibilities:
Conduct feasibility analyses by querying healthcare datasets to assess patient cohort availability based on complex inclusion/exclusion criteria (i.e. procedures, diagnoses, diversity, longitudinal completeness, regulatory constraints)
Collaborate directly with customers to understand their use cases and support effective data integration
Ensure customers have a clear understanding of the data’s structure, limitations, and strengths
Identify gaps in our data offerings and provide insights to our partnerships team on the highest-priority data acquisitions
Evaluate potential data partnerships, ensuring the data is high-quality, well-documented, and commercially viable
About You:
You are curious, tenacious, and proactive
You are not bothered by ambiguity but embrace finding patterns in complex environments
Undergraduate or MS/BS + industry experience in a quantitative field such as mathematics, economics, statistics, biostatistics, bioinformatics, computer science, or data science
Proficiency with programming in R/Python/SQL
Hands-on experience working with large-scale healthcare datasets, including one or more of the following: imaging, EHR, genomics, claims, or pathology data
Team-player, no job is too big or too small
You are an eager researcher and you are not afraid to learn or face a knowledge pit.
You treat those around you with kindness
Bonus if you have these attributes:
Experience in a customer facing role
Experience with data optimization techniques such as model-based filtering, multimodal data integration, heuristic filtering, and/or target distribution matching
Experience applying machine learning or logistic regression techniques to healthcare data
Familiarity with third-party data certification or audit processes related to privacy and data quality
Ability to think creatively and insightfully about large scale data problems