Senior Applied Data Scientist, Fleet Intelligence

Fleetio

10d • Remote

About The Position

Fleetio is seeking a product-minded Senior Applied Data Scientist to join their Fleet Intelligence team. This role involves transforming fleet maintenance and operational data into actionable intelligence to help customers make better decisions regarding usage, cost, availability, maintenance risk, and asset lifecycle. It's an applied position at the intersection of data science, machine learning, analytics engineering, and product development. The scientist will collaborate with Product Managers, Designers, Software Engineers, and Data partners to identify prediction problems, develop models, and integrate them into customer-facing workflows. The primary goal is to deliver intelligence that influences decisions, is timely, and honestly communicates uncertainty. Initial work will focus on confirmed Fleet Intelligence areas like utilization and tire intelligence, ROI measurement, existing predictive models, and supporting analytics foundations. The role will involve assessing current model quality, establishing baselines, and improving data-science practices while shipping useful capabilities. Over time, the scientist will help evaluate and shape future Predictive Fleet Intelligence opportunities, determining their technical credibility, customer value, and readiness for product investment.

Requirements

• 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, 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.

Nice To Haves

• 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.

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.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed