Note: The job is a remote job and is open to candidates in USA. Fleetio is seeking a product-minded Senior Applied Data Scientist for its Fleet Intelligence team. The role develops and operationalizes models and customer-facing intelligence from fleet data, partnering across Product, Design, Engineering, and Data to support better fleet decisions.

Responsibilities

• 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

Skills

• 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
• 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
• 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

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
• Parental leave - birthing parent (16 weeks paid) non-birthing (4 weeks paid)
• FSA & HSA options
• Short and long term disability (short term 100% paid)
• Community service funds
• Professional development funds
• Wellbeing fund - $150 quarterly
• Business expense stipend - $125 quarterly
• Mac laptop + new hire equipment stipend
• Fully stocked kitchen with tons of drinks & snacks (BHM only)
• Remote working friendly since 2012

Company Overview

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