Senior Applied Data Scientist, Fleet Intelligence

Important: 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. 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.
• Define model-quality metrics, confidence thresholds, drift detection, and feedback loops appropriate to the cost and reversibility of the customer decision.
• Partner with Product and Design to make model outputs understandable, explainable, and actionable inside the workflows where customers already make decisions.
• Establish reusable practices for experimentation, model documentation, validation, monitoring, and responsible claims about predictive performance.
• Communicate findings, tradeoffs, risks, and recommendations clearly to technical partners, product leaders, and executives.
• Share knowledge through design reviews, documentation, pairing, and mentorship across Fleet Intelligence and adjacent teams.

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 DBT, Python, and SQL, including exploratory analysis, feature engineering, model development, and evaluation on large datasets.
• Strong grounding in statistics and machine learning fundamentals, including model selection, validation, calibration, uncertainty, bias, and error analysis.
• Experience with time-series forecasting, regression, classification, ranking, anomaly detection, survival or reliability analysis, or optimization; depth in several of these areas is more important than breadth across all of them.
• Experience taking models beyond notebooks into reliable production workflows, including versioning, testing, deployment, observability, performance monitoring, and retraining or refresh strategies.
• Experience using modern cloud data platforms and transformation workflows such as Snowflake, dbt, and orchestration tools in support of applied modeling work.
• Ability to identify data-quality limitations, recommend improvements, and collaborate with data engineers on pipelines and source reliability.
• Excellent written and verbal communication, particularly when explaining complex methods, uncertainty, and tradeoffs to non-specialists.
• 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 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.
• Experience contributing to customer-facing software products or collaborating closely with full-stack product engineers.
• Graduate study in statistics, data science, computer science, operations research, applied mathematics, economics, or a related quantitative field.

This application is not intended to and does not create a contract or offer of employment. Employment with Fleetio is at will.

If you have a disability or a special need that requires an accommodation to fill out the online application, please let us know by calling (205) 718-7500.