Senior Applied Data Scientist, Fleet Intelligence

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 goal is to deliver intelligence that changes a decision, arrives early enough to act on, and communicates uncertainty honestly.

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. You will assess current model quality, establish credible baselines, and help the team ship useful capabilities while strengthening its data-science practices.

Over time, you will help evaluate and shape opportunities for Predictive Fleet Intelligence, including projection, anticipation, and risk inference. You will help determine which opportunities are technically credible, valuable to customers, and ready to become durable product investments.

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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.