Senior Applied Data Scientist, Fleet Intelligence - Fleetio

About Fleetio

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!

About the Role

Fleetio is looking for a product-minded Senior Applied Data Scientist to join our Fleet Intelligence team. You will help turn years of fleet maintenance and operational data into trusted, actionable intelligence that helps customers anticipate what is ahead and make better decisions about usage, cost, availability, maintenance risk, and asset lifecycle.

This is an applied role at the intersection of data science, machine learning, analytics engineering, and product development. You will work closely with Product Managers, Designers, Software Engineers, and Data partners to identify valuable prediction problems, develop practical models, and bring them into customer-facing workflows.

Your initial mandate will be grounded in confirmed Fleet Intelligence work: utilization and tire intelligence, ROI measurement, existing predictive models, and the analytics foundations needed to support customer-facing intelligence. Over time, you will help evaluate and shape opportunities for Predictive Fleet Intelligence, including projection, anticipation, and risk inference.

This is a remote opportunity and is open to candidates in the United States, Canada, or Mexico.

Who You Are

• 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, and are comfortable communicating confidence intervals, limitations, and data gaps to technical and non-technical partners.
• You collaborate well with cross-functional teams while independently building production-quality models and guiding how model outputs appear in-product.
• You are pragmatic, product-minded, and outcome-oriented.

Your Impact

• Help deliver near-term Fleet Intelligence initiatives, including tire intelligence, utilization intelligence, ROI measurement, existing predictive models, and analytical foundations.
• Evaluate and develop credible projections or predictive models for fleet usage, maintenance cost, availability, condition/failure risk, and asset lifecycle decisions.
• 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 data gaps.
• Build, validate, and operationalize models from experimentation through production monitoring and iteration.
• Define model-quality metrics, confidence thresholds, drift detection, and feedback loops.
• Partner with Product and Design to make model outputs understandable, explainable, and actionable.
• Establish reusable practices for experimentation, model documentation, validation, and monitoring.
• Communicate findings, tradeoffs, risks, and recommendations clearly to leadership and executives.

Your Experience

• 5+ years of experience in applied data science, machine learning, statistical modeling, or a closely related role.
• A track record of developing and shipping models or decision-support systems that influenced real customer or business outcomes.
• Strong proficiency with Python and SQL, including exploratory analysis, feature engineering, model development, and evaluation on large datasets.
• Strong grounding in statistics and machine learning fundamentals (model selection, validation, calibration, uncertainty, bias, and error analysis).
• Experience with time-series forecasting, regression, classification, ranking, anomaly detection, survival/reliability analysis, or optimization.
• Experience taking models beyond notebooks into reliable production workflows (versioning, testing, deployment, observability, performance monitoring, retraining).
• Experience using modern cloud data platforms and transformation workflows such as Snowflake, dbt, and orchestration tools.
• Ability to identify data-quality limitations and collaborate with data engineers on pipeline reliability.
• Excellent written and verbal communication skills.
• Experience working cross-functionally with Product, Design, Software Engineering, and Data Engineering.

Considered a Plus

• Experience with fleet, transportation, maintenance, reliability, asset management, insurance, logistics, or operational domains.
• 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.
• Experience contributing to customer-facing software products.
• Graduate study in statistics, data science, computer science, operations research, applied mathematics, economics, or a 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)
• 12 company holidays + 2 floating holidays
• Paid parental leave (16 weeks birthing, 4 weeks non-birthing)
• FSA & HSA options
• Short and long term disability (short term 100% paid)
• Professional development & community service funds
• Wellbeing fund ($150 quarterly)
• Business expense stipend ($125 quarterly)
• Mac laptop + new hire equipment stipend