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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