Senior Applied Data Scientist, Fleet Intelligence

Remote: yes Posted: 2026-09-11

A little about us…Fleetio is a modern software platform that helps thousands of organizations worldwide manage their fleet operations. Transportation technology is a hot market, and we’re leading the charge with raving fans and new customers signing up every day. We raised $450M in our Series D funding round in March of 2025 and are on an exciting trajectory as a company. Fleetio is also a proud founding member of the Rails Foundation!

You are an applied data scientist who enjoys working on ambiguous, high-value product problems. You can translate a customer or business decision into a measurable modeling problem, establish a credible baseline, and iteratively improve it. You know when straightforward statistics or deterministic projection is the right answer and when a machine learning approach is warranted.

You care deeply about correctness, explainability, and trust. You are comfortable communicating confidence intervals, limitations, and data gaps to technical and non-technical partners. You collaborate well with software and data engineers, but you can independently explore data, build production-quality models, define evaluation methods, and guide how model outputs should appear in a product experience.

You are pragmatic, product-minded, and outcome-oriented. You would rather ship a useful, well-calibrated forecast than an impressive model that does not change a customer decision.

Your Impact

- Help deliver near-term Fleet Intelligence initiatives, including tire intelligence, utilization intelligence, ROI measurement, existing predictive models, and the analytical foundations that support customer-facing intelligence.

- Evaluate and develop credible projections or predictive models for fleet usage, maintenance cost, availability, condition and failure risk, and asset lifecycle decisions as product direction and evidence mature.

- Translate product questions into clear hypotheses, target variables, baselines, evaluation plans, and incremental delivery milestones.

- Explore Fleetio’s maintenance, usage, cost, work-order, telematics, warranty, and asset-history data to identify predictive signals and material data gaps.

- Build, validate, and operationalize models from experimentation through production monitoring and iteration.