About Doceree :

Doceree is the world's only AI-powered Operating System for healthcare marketing. We aim to be a catalyst for change, improving healthcare by enabling more meaningful interactions with HCPs. Our patented AI brings deeper context to every HCP touchpoint, understanding who HCPs are, what they're exploring, how they engage, and when they make decisions, all in a privacy-compliant way.

About The Job :

We are looking for a hands-on Senior Data Scientist to lead a small, high-impact team of Data Scientists while continuing to contribute technically. This is not a coordination-only management role. You will help shape modeling approaches, review code and experiments, unblock productionization, and improve the operating cadence of the team.

Key Responsibilities :

- Extract entities and attributes from heterogeneous sources such as warehouse tables, registries, and feeds.

- Design and implement probabilistic entity linking and deduplication across large, inconsistent healthcare datasets.

- Own the linking pipeline end-to-end, including pairwise and graph-based scoring, calibrated match probabilities, and resolution into stable entities.

- Define link quality metrics : precision and recall, calibration, error analysis, and confidence thresholds.

- Link entities and attributes to controlled medical vocabularies (specialties, conditions, products).

- Use graph representations and graph ML as core linking tools (clustering, centrality, community detection, link prediction, and embeddings).

- Build and maintain the data and graph estate, including warehouse transformation, monitoring, and algorithm performance optimization.

Qualifications :

- 4+ years shipping production ML or data-science work, including hands-on entity linking, entity resolution, or record linkage at scale.

- Languages : Python (production-grade, tested, type-hinted), strong SQL.

- Software Engineering : FastAPI, Docker, pytest, Git, and CI/CD.

- Probabilistic Methods : Match scoring, calibration, threshold selection, and evaluation.

- Graphs : Experience with graph algorithms (connected components, centrality) and graph ML (embeddings, GNNs).

- Data & ML Engineering : Warehouse-centric pipelines, ETL, scalable blocking, vector search, and incremental refresh.

- Stack : Snowflake (or similar distributed SQL warehouse), PyTorch Geometric/DGL, igraph/NetworkX, Splink, and a graph store (e.g., Neptune, Neo4j).

Nice to Have :

- Knowledge of medical ontologies (MeSH, UMLS, SNOMED, RxNorm).

- Experience with prescribing/claims data, NPPES/NPI, PubMed, or ClinicalTrials.gov.

- Biomedical NLP, AdTech experience, and regulated-environment work with PII constraints.