Build and validate models and patterns that help applications make better decisions in the moment.
Partner with engineering and governance to build the Event Mesh, the enterprise trigger and intent capability that converts messy signals into normalized events.
Build, rent, or buy document intelligence capabilities that turn unstructured documents into deterministic, structured outputs.
Develop models that shape the advisor, assistant, and operations day by prioritizing work, recommending next steps, and reducing avoidable rework.
Operate a fast-cycle experimentation function that moves from hypothesis to validated proof in weeks.
Establish rigorous evaluation methods for ML and LLM-enabled capabilities.

A Bachelor’s degree in Computer Science, Statistics, Machine Learning, or a related quantitative field.
A Master’s or PhD is preferred, or equivalent experience.
10 or more years of experience in data science or machine learning, with 5+ years leading teams delivering production capabilities.
Proven track record building and validating ML systems used in real products, not just research prototypes.
Proven experience with modern ML tooling and stacks (Python, PyTorch or TensorFlow, common transformer tooling, and cloud ML platforms).
Experience with retrieval-augmented generation (RAG), embedding-based retrieval, and vector stores to ground model outputs in enterprise knowledge, with attention to evaluation, freshness, and traceability.

Data Science Solutions

Key skills