Remote opportunity at
FleetioSenior Applied Data Scientist, Fleet Intelligence
Fleetio is seeking a Senior Applied Data Scientist for its Fleet Intelligence group. The position is remote and open to applicants based in the US, Canada…
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About This Role
Fleetio is seeking a Senior Applied Data Scientist for its Fleet Intelligence group. The position is remote and open to applicants based in the US, Canada or Mexico. Fleetio provides a platform that organizations use to run their vehicle fleets, and the company reports a $450 million Series D round in March 2025. The work converts years of maintenance, usage and cost records into insight that helps customers decide how…
Job Description
Fleetio is seeking a Senior Applied Data Scientist for its Fleet Intelligence group. The position is remote and open to applicants based in the US, Canada or Mexico. Fleetio provides a platform that organizations use to run their vehicle fleets, and the company reports a $450 million Series D round in March 2025.
The work converts years of maintenance, usage and cost records into insight that helps customers decide how to deploy vehicles, control spending, keep units available and lower maintenance risk. Early priorities include tire and utilization analytics, return-on-investment measurement, the predictive models already in place, and the data groundwork that customer-facing features require. Later on, the scientist will help assess projection, anticipation and risk-inference ideas.
Day to day, the scientist teams up with PMs, designers, engineers and data peers. Tasks include judging how good current models are, setting realistic baselines, and building and monitoring production models with clear quality metrics, drift checks and feedback loops. The scientist also makes sure model results are easy to understand and act on inside the screens where customers already make choices.
A strong fit is an applied scientist who enjoys murky product questions, can tell when basic statistics are enough and when machine learning is warranted, values accuracy and explainability, and can describe uncertainty to people without a technical background. The posting favors a dependable, well-calibrated forecast over an impressive model that leaves customer decisions unchanged.
Responsibilities
- Deliver near-term Fleet Intelligence projects covering tires, utilization and return-on-investment tracking
- Assess projections and predictive models for usage, upkeep spending, availability, breakdown risk and asset life
- Convert product questions into testable hypotheses, target variables, baselines and evaluation plans
- Mine maintenance, usage, spend and telematics records for predictive signals and data gaps
- Build, check and run models from early experiments through live monitoring
- Set quality measures, confidence cutoffs, drift alerts and feedback mechanisms
- Work with product and design so model outputs are clear and usable within customer workflows
- Pass knowledge along through design reviews, written documentation, pair work and coaching
Requirements
- Five-plus years working in applied data science, ML, statistical modeling or an adjacent discipline
- Track record of shipping models or decision-support tools that moved customer or business results
- Working fluency in dbt, Python and SQL on very large datasets
- Sound command of statistics and ML, covering validation, calibration, uncertainty and error analysis
- Background in time-series prediction, regression, classifiers, ranking systems, outlier detection, reliability analysis or optimization, ideally deep in several
- Experience moving models into production, including version control, testing, deployment, observability and retraining cadences
- Practice with cloud data warehouses and transformation and orchestration tools such as Snowflake
- Skill at spotting data-quality problems and partnering with data engineers on pipelines
- Clear communicator, especially about methods, uncertainty and trade-offs with non-specialists
Qualifications
- Domain knowledge from vehicle operations, transport, insurance, logistics or other asset-heavy industries
- Prior modeling of upkeep spend, breakdown likelihood, remaining service life, warranty exposure, usage, demand or replacement timing
- Hands-on use of semantic layers and analytics platforms such as ThoughtSpot or Cube
- Experience evaluating recommendations through alternative designs when randomized trials are not practical
- Experience shipping customer-facing software or partnering tightly with full-stack product developers
- Advanced degree in a quantitative subject such as statistics, computing, operations research or economics
Core Skills
Benefits
- Health and dental plans in several tiers, with employee coverage paid at 100 percent and family coverage at 50 percent
- Vision coverage and incentive stock options
- 401(k) with a 4 percent match
- Four weeks of PTO that rises after year two, plus 12 company holidays and 2 floating days off
- Paid parental leave of 16 weeks for birthing parents and 4 weeks for other parents
- FSA and HSA accounts, plus short- and long-term disability cover, with short-term benefits fully paid
- Funds for community service and professional growth
- Wellbeing allowance of $150 each quarter and an expense stipend of $125 each quarter
- Mac laptop with a stipend for new hire equipment
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where can candidates be located for this role?
The role is remote, and Fleetio accepts applicants based in the US, Canada or Mexico.
What experience is required?
The posting asks for at least five years of applied data science or ML work, or a closely related background, plus a record of models that shaped real outcomes.
Does the posting state a salary?
The posting does not specify a salary or pay range.
How do I apply?
The posting does not describe the application steps. It notes that candidates who need an accommodation to complete the online application can call the company for help.
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