Agero's Motor Club handles millions of roadside events every year. We're piloting a next-gen Dispatch System that fuses short and long-term horizon optimizers to decide who gets which job, when, and why. You will be instrumental in developing, evaluating and productionizing this system, building out the prediction models, incorporating them into the optimizer and leading the team to a successful outcome.
Requirements
- 6 + yrs combined experience in Data Science and ML Engineering with ownership of production systems.
- Expert-level Python.
- Hands-on optimization (Mixed Integer Programming / Linear Optimization / Stochastic Optimization) and modern ML (XGBoost, PyTorch).
- Proven record designing cloud-native pipelines on AWS, GCP or Azure (AWS).
- Strong SQL, feature-store design, and data-quality mindset.
- Ability to translate business objectives into mathematically rigorous experiments.
- Familiarity with Monte-Carlo tree search, multi-agent simulation, or hierarchical RL.
Responsibilities
- Architect & ship: Design end-to-end Python services (batch + streaming) that ingest model outputs, run constrained optimisation, and surface real-time dispatch decisions.
- Model & simulate: Build/extend ML models (gradient-boosting, deep learning, OR-Tools) and run time-horizon simulations to quantify cost vs. service-level trade-offs.
- Operationalise: Automate training, validation, A/B rollout, and monitoring (SageMaker / Airflow).
- Lead & collaborate: Partner with Product, Ops, and Data Engineering; mentor a small squad of DS/ML engineers; present findings to execs.
- Continuously improve: Instrument NPS / cost telemetry, identify failure modes, and iterate.
Other
- Willingness to travel is required, as you may need to attend on-site team meetings from time to time.
- Reach out with a link to your GitHub or a brief project write-up.
