Senior Applied Data Scientist

Job Overview

Fleetio is seeking a product-minded Senior Applied Data Scientist to join their Fleet Intelligence team. This role sits at the intersection of data science, machine learning, analytics engineering, and product development. The goal is to transform years of fleet maintenance and operational data into actionable intelligence that helps customers anticipate decisions regarding usage, cost, availability, maintenance risk, and asset lifecycle.

Key Responsibilities

You will:

• Collaborate with Product Managers, Designers, Software Engineers, and Data partners to identify valuable prediction problems and develop practical models.
• Deliver intelligence that changes decisions, arrives early, and communicates uncertainty honestly.
• Assess current model quality, establish credible baselines, and ship useful capabilities while strengthening data-science practices.
• Evaluate and shape opportunities for Predictive Fleet Intelligence, including projection, anticipation, and risk inference.
• Determine which opportunities are technically credible, valuable to customers, and ready for product investment.

Initial Mandate

Your initial focus will be on:

• Utilization and tire intelligence
• ROI measurement
• Existing predictive models
• Analytics foundations for customer-facing intelligence

Who You Are

You are an applied data scientist who:

• Enjoys solving ambiguous, high-value product problems.
• Can translate customer/business decisions into measurable modeling problems and iteratively improve them.
• Knows when statistics or deterministic projection is appropriate and when machine learning is warranted.
• Prioritizes correctness, explainability, and trust in models.
• Communicates confidence intervals, limitations, and data gaps effectively to both technical and non-technical stakeholders.
• Collaborates with software/data engineers but can independently explore data, build production-quality models, and guide model integration into product experiences.
• Is pragmatic, product-minded, and outcome-oriented—preferring to ship useful, well-calibrated forecasts over impressive but irrelevant models.

Your Impact

• Help deliver near-term Fleet Intelligence initiatives, including tire intelligence, utilization intelligence, ROI measurement, and predictive models.
• Develop credible projections for fleet usage, maintenance cost, availability, condition, failure risk, and asset lifecycle decisions.
• Translate product questions into clear hypotheses, target variables, baselines, evaluation plans, and incremental milestones.
• Explore Fleetio’s data (maintenance, usage, cost, work-orders, telematics, warranty, asset history) to identify predictive signals and data gaps.
• Build, validate, and operationalize models from experimentation to production monitoring.
• Define model-quality metrics, confidence thresholds, drift detection, and feedback loops.
• Partner with Product and Design to make model outputs understandable, explainable, and actionable in customer workflows.
• Establish reusable practices for experimentation, model documentation, validation, monitoring, and responsible claims.
• Communicate findings, tradeoffs, risks, and recommendations to technical partners, product leaders, and executives.
• Share knowledge through design reviews, documentation, pairing, and mentorship.

Your Experience

• 5+ years in applied data science, machine learning, statistical modeling, or a related role.
• A track record of developing and shipping models that influenced real customer/business outcomes.
• Strong proficiency in 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/reliability analysis, or optimization.
• Experience taking models from notebooks to production, including versioning, testing, deployment, observability, performance monitoring, and retraining.
• Experience with modern cloud data platforms (e.g., Snowflake, dbt) and orchestration tools.
• Ability to identify data-quality limitations and collaborate with data engineers on pipelines and source reliability.
• Excellent written and verbal communication, especially explaining complex methods to non-specialists.
• Experience working cross-functionally with Product, Design, Software Engineering, and Data Engineering.

Considered a Plus

• Experience in fleet, transportation, maintenance, reliability, asset management, insurance, logistics, or related domains.
• Experience modeling maintenance cost, equipment failure, remaining useful life, warranty exposure, utilization, demand, or asset replacement.
• Familiarity with semantic layers and analytics tools (e.g., ThoughtSpot, Cube).
• Experience designing experiments or evaluating recommendations when randomized testing is impractical.
• Experience contributing to customer-facing software products or collaborating with full-stack engineers.
• Graduate study in statistics, data science, computer science, operations research, applied mathematics, economics, or related fields.