[Hiring] Lead Data Scientist / Data Analyst @EPAM Systems

Role Description

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.

• 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

Qualifications

• 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)

Requirements

• 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