Senior Applied Data Scientist, Fleet Intelligence
About this role
Fleetio needs to turn years of operational and maintenance records into trusted intelligence that helps organizations anticipate maintenance risks and manage asset lifecycles effectively. The Fleet Intelligence team works at the intersection of data science, machine learning, and product development to deliver insights that change customer decisions in time to act.
Incoming team members will tackle utilization and tire intelligence, ROI measurement, and foundational analytics to support customer-facing features. Responsibilities involve evaluating model quality, building predictive workflows, and collaborating with software engineers and product managers to integrate insights directly into user interfaces. Success in the role requires a pragmatic approach that values clear uncertainty communication and actionable forecasts over overly complex algorithms.
What you will do
- Deliver near-term initiatives including tire intelligence, utilization analytics, ROI measurement, and core analytical foundations for customer-facing features.
- Translate product questions into clear hypotheses, target variables, baselines, evaluation plans, and incremental delivery milestones.
- Explore maintenance, usage, cost, telematics, warranty, and asset-history data to identify predictive signals and address data gaps.
- Build, validate, and operationalize models from initial experimentation through production monitoring and iterative improvement.
- Define model-quality metrics, confidence thresholds, drift detection, and feedback loops appropriate to the cost of customer decisions.
- Partner with product and design teams to make model outputs understandable, explainable, and actionable inside existing user workflows.
- Establish reusable practices for experimentation, model documentation, validation, monitoring, and responsible performance claims.
What you need
- Have at least five years of experience in applied data science, machine learning, statistical modeling, or a closely related discipline.
- Demonstrate a track record of developing and shipping models or decision-support systems that influenced real customer or business outcomes.
- Bring strong proficiency with Python, SQL, and dbt for exploratory analysis, feature engineering, and model development on large datasets.
- Maintain a strong grounding in statistics and machine learning fundamentals, covering model selection, validation, calibration, and uncertainty.
- Possess experience with time-series forecasting, regression, classification, ranking, anomaly detection, or survival analysis.
- Know how to take models beyond notebooks into reliable production workflows with proper versioning, testing, deployment, and observability.
- Use modern cloud data platforms and transformation workflows such as Snowflake, dbt, and orchestration tools for applied modeling work.
- Communicate findings, tradeoffs, risks, and recommendations clearly to technical partners, product leaders, and executives.
Nice to have
- Experience with fleet, transportation, maintenance, reliability, asset management, insurance, logistics, or another operational domain
- Experience modeling maintenance cost, equipment failure, remaining useful life, warranty exposure, utilization, demand, or asset replacement decisions
- Familiarity with semantic layers and analytics tools such as ThoughtSpot or Cube
- Experience designing experiments or evaluating recommendations when randomized testing is impractical
- Graduate study in statistics, data science, computer science, operations research, applied mathematics, economics, or a related quantitative field
Benefits
- Multiple health/dental coverage options (100% coverage for employee, 50% for family)
- Vision insurance
- Incentive stock options
- 401(k) match of 4%
- PTO - 4 weeks (increases at year two!)
- Wellbeing fund - $150 quarterly
Skills and tech stack
Questions candidates ask
Is this role remote?
This is a remote opportunity and is open to candidates in the United States, Canada, or Mexico.
What tools and technologies will I use?
You will work with Python, SQL, dbt, and modern cloud data platforms such as Snowflake to build and operationalize models.
What will I work on initially?
Your initial mandate will be grounded in confirmed Fleet Intelligence work, including utilization and tire intelligence, ROI measurement, existing predictive models, and analytics foundations.
Who will I collaborate with on the team?
You will work closely with Product Managers, Designers, Software Engineers, and Data partners to identify valuable prediction problems and bring practical models into customer-facing workflows.