Key Responsibilities

Deploy into production use cases with considerable business impact, moving from "science experiments" to definitive ROI
Work on rigorous head-to-head benchmarking against client baselines (XGBoost, LightGBM), executing data engineering, feature engineering, and validation
Collaborate with research and product teams to translate operational pain points and data anomalies into essential inputs for the product roadmap
Identify the right business problems, prevent data leakage, and handle "last mile" integration (VPC, on-prem, air-gapped)
Collaborate with Sales and Solution Architect teams to align diverse stakeholders and explain predictions to business users

Requirements

PhD or Master's in CS, Math, Stats, or equivalent deep statistical literacy
2+ years as a technical individual contributor (data scientist or software engineer)
Experience with containerization (Docker), orchestration, and writing performant APIs (FastAPI/Flask)
Mastery of the end-to-end pipeline: framing, pre-processing, ML algorithms, and validation strategies
Deep understanding of data handling (PySpark, Pandas) and memory optimization
Demonstrated experience optimizing models for specific business problems
Strong communication skills with ability to translate architectural nuances into clear business value

Experience with PyTorch and cloud-native ML pipelines (AWS, GCP, Azure)
Experience as a Forward Deployed Engineer, Staff Engineer, Machine Learning Engineer, or Staff Data Scientist
Industry-based subject matter expertise