• Design, train, and validate predictive and statistical models that turn noisy healthcare data into reliable intelligence products used by MedTech commercial teams. • Frame open-ended business questions as modeling problems — selecting the right approach (classification, regression, clustering, causal inference, ensembles, etc), defining success metrics, and quantifying uncertainty. • Engineer features and conduct applied research across time-series, geospatial, demographic, insurance claims, and more datasets, to improve the coverage and signal quality of our core data assets. • Own the full model lifecycle: exploratory analysis, baseline modeling, experimentation, validation, deployment, and post-launch monitoring for drift and performance. • Partner with product managers and cross-functional stakeholders to translate customer problems into model-backed product features and to shape the roadmap. • Provide technical leadership and mentorship on statistical and ML methodology for engineers and analysts across the Data organization and across all of AcuityMD. • Document models, assumptions, and data contracts so results are interpretable and reproducible for internal and external audiences.

• Work closely with senior technologists to train, optimize and evaluate vehicle-oriented Machine Learning models • Work cross functionally with Perception, Behavior, Integration, Test and Data teams to ensure the highest quality in model performance and deployment

• Design, train and evaluate state of the art models for May’s autonomous driving, simulation and ML Platform stack. • Leverage emerging techniques in the End-to-End driving, Vision Language Action (VLA), World or Foundation model domains to solve commercial-scale problems. • Lead small teams of cross functional Engineers beyond the state of the art. • Define data balance, training experiment and evaluation practices to train efficiently at petabyte scale.

• Build a high-performing ML team: Lead hiring, onboarding, performance management, and career development. Set the frameworks, operating rhythms, and succession plans that give ML engineers clarity, support, and room to grow. You are the person who makes sure the team is getting the leadership attention it needs. • Own the team's operating systems: Build and maintain the rituals and structures that keep the team effective: sprint cadences, incident review, model monitoring feedback loops, cross-team reporting, and the prioritisation processes that keep the team focused on what matters. • Enable technical excellence without owning it: Act as a sounding board for ML engineers on questions and blockers. Ask the right questions, help surface risks, and create space for the experts to make good decisions, without adding another review layer or positioning yourself as a technical authority. • Drive collaboration with the Rosso Engineering Manager: Partner closely to align priorities between the ML and software engineering teams within the service. The two teams need to work together effectively, and you are a key part of making that happen. • Represent the team and champion tem's culture: Communicate progress, risks, and priorities clearly to the broader organisation. Lead with intention in a fully remote environment, champion what excellent remote work looks like, and represent tem in the ML community when recruiting and engaging externally.