Lead Data Scientist / Data Analyst with Animal Healthcare background

We are seeking a Lead Data Scientist / Data Analyst to design, validate, and integrate scalable ML solutions that standardize clinical signalment attributes and generate health-predisposition insights in high-volume data pipelines. You will lead deterministic-first modeling, rigorous evaluation, and production transition with strong documentation and governance. Responsibilities Lead design and delivery of optimized models for species, gender, breed, weight classification, and age classification Build health-predisposition insights models using standardized signalment attributes and governed ontologies Apply a deterministic-first approach and use machine learning primarily for high-ambiguity scenarios Define class-frequency tiers, calibrated thresholds, and evaluation plans that go beyond overall accuracy Produce confidence scores, method traces, exception flags, and model versioning artifacts for each model Create model cards and documentation that support governance, reproducibility, and transition to operations Validate performance across common, rare, ambiguous, and clinically significant cases using appropriate classification metrics Incorporate animal-health domain knowledge into life-stage thresholds, breed-dependent reference ranges, and risk mappings Implement training and evaluation pipelines in Databricks with MLflow tracking and reproducible builds Collaborate with SMEs to refine labels, interpret validation feedback, and adjust modeling strategies Support integration into production data pipelines and contribute to stabilization and knowledge transfer Requirements 5+ years of experience in data science and machine learning for classification problems Hands-on experience building hybrid deterministic + ML solutions for ambiguous cases Lead-level ownership of end-to-end model delivery from prototyping through stabilization and knowledge transfer Strong project execution skills in fixed-scope, milestone-driven delivery with multiple parallel model tracks Hands-on Python.ML skills for feature engineering, training, and evaluation workflows Databricks and MLflow experience for reproducible training, evaluation, and model versioning Computer vision and natural language processing experience to support fuzzy matching and embedding-based approaches OMOP Common Data Model knowledge and SNOMED CT Veterinary Extension concept-mapping experience Strong analytical skills to define class-frequency tiers, calibrated risk bands, and performance thresholds Strong communication skills to interpret annotation and validation plans with domain SMEs and translate them into model updates Advanced English proficiency (C1+ level) Nice to have Expertise with classification metrics for imbalanced data such as PR AUC, F1 by class, and balanced accuracy Experience using calibrated probability and reliability diagrams to assess confidence scores Ability to set per-tier metric targets and acceptance criteria for rare and clinically significant classes #J-18808-Ljbffr EPAM Systems Inc