The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems.
Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers.
RESPONSIBILITIES
Decide priorities across the functions with their leads, and review the technical trade-offs yourself.
Manage the leads who run these functions and hold each of them to what their team has committed to.
Stay close to production. Own delivery dates, reliability, and what happens when something breaks.
Own the infrastructure for model training, serving, and large scale data processing, including its cost.
Decide which numbers (KPIs) the group is measured on, then track them and report them.
Report the group's progress and risks to senior management, to product, and to the commercial teams.
Work with the applied research team to move their results into production.
Hire and keep senior engineers, and decide how the group is staffed.
Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring.
Requirements: You'll be a great fit if you have:
8+ years in engineering, with 5+ years leading teams and managers.
Worked in a multi group environment, leading projects that involve inter group dependencies
Production experience with time series forecasting, reinforcement learning, or large scale optimization.
Experience setting the direction for a machine learning or data engineering group
Enough depth in production ML systems to review architecture decisions with your senior engineers.
Experience holding managers to delivery dates and to the numbers their teams are measured on.
Hands-on familiarity with Python, PyTorch, Google Cloud Platform (GCP), and orchestration frameworks such as Dask and Dagster.
A Bachelor's degree in Computer Science, Engineering, Mathematics, or a relevant field.
The ability to explain a technical decision to a commercial audience, and a commercial constraint to your engineers.
NICE TO HAVE
An MSc or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a relevant field.
Experience taking a machine learning platform from an early prototype to something a business runs on.
Experience in airlines, travel, revenue management, or dynamic pricing.
Familiarity with real time inference and high volume data processing.
This position is open to all candidates.