[All remote jobs](https://jobicy.com/jobs.md)[![Fleetio logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2023/07/e571b83c37e0ed5371754d319be16e76.jpeg)](https://jobicy.com/company/fleetio.md)[Fleetio](https://jobicy.com/company/fleetio.md) # Senior Applied Data Scientist, Fleet Intelligence Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/fleetio.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Canada](https://jobicy.com/job-region/canada.md), [Mexico](https://jobicy.com/job-region/mexico.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished14 Oct 2026Apply before13 Nov 2026Listing views46Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Fleetio seeks a senior applied data scientist to build customer-facing intelligence for fleet operations, including utilization, tire insights, maintenance costs, availability, and failure risk. The role combines statistical modeling, machine learning, analytics engineering, and product development, with ownership from problem framing through production monitoring. The successful candidate will establish baselines, validate and calibrate models, communicate uncertainty, and make model outputs actionable within product workflows. Strong Python, SQL, dbt, Snowflake, forecasting, and cross-functional product collaboration experience are central to the role. It is a remote position open to candidates in the United States, Canada, and Mexico. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior, ambiguous applied-science role requiring production-quality modeling across complex operational data and high-stakes customer decisions. The candidate must independently prioritize credible opportunities while explaining uncertainty and tradeoffs to technical and non-technical stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$140k–$200k0$220.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual cadence is inferred from the senior full-time professional role. Estimated USD annual market compensation for a senior applied data scientist in the eligible North American markets is approximately $140,000–$200,000, with an estimated midpoint of $165,000. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied Data Science](https://jobicy.com/jobs?search_keywords=Applied%20Data%20Science.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Statistical Modeling](https://jobicy.com/jobs?search_keywords=Statistical%20Modeling.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Time-Series Forecasting](https://jobicy.com/jobs?search_keywords=Time-Series%20Forecasting.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[Model Validation](https://jobicy.com/jobs?search_keywords=Model%20Validation.md)[Model Monitoring](https://jobicy.com/jobs?search_keywords=Model%20Monitoring.md)[Predictive Analytics](https://jobicy.com/jobs?search_keywords=Predictive%20Analytics.md)[Data Quality](https://jobicy.com/jobs?search_keywords=Data%20Quality.md)[Experiment Design](https://jobicy.com/jobs?search_keywords=Experiment%20Design.md)[Product Analytics](https://jobicy.com/jobs?search_keywords=Product%20Analytics.md) Sample interview questionsHow would you approach developing a model to predict fleet maintenance cost or failure risk?I would begin by defining the customer decision, prediction horizon, target variable, and cost of false positives and false negatives. I would audit available maintenance, work-order, telematics, warranty, and usage data; establish simple historical or rules-based baselines; then evaluate appropriately calibrated forecasting, regression, classification, or survival models. I would validate performance by fleet segment and time period, document limitations, and only productionize outputs that are actionable and reliably monitored. How do you decide when a simple statistical method is preferable to a machine learning model? I compare methods against the decision requirement rather than optimizing complexity. A simple method is preferable when it is sufficiently accurate, easier to explain, more robust to sparse or changing data, and faster to operate. I use a more complex model only when it produces a meaningful, validated improvement that changes customer outcomes enough to justify the additional operational and interpretability cost. What would you include in a production model-monitoring plan? I would monitor input-data quality and freshness, feature distributions, prediction distributions, service reliability, realized outcomes, and model performance once labels mature. The plan would include drift thresholds, segmented evaluation, alerting ownership, retraining or refresh criteria, rollback procedures, and clear documentation of intended use and known limitations. I would also monitor calibration, especially where customers need confidence estimates to make decisions. How would you communicate uncertainty in a prediction to a non-technical product stakeholder? I would explain the expected range of outcomes, the confidence level, the key drivers, and the practical implication for the decision. For example, rather than presenting a single maintenance-cost estimate as certain, I would show a range and identify whether the recommendation is strong enough to act on now or should trigger additional review. I would avoid technical jargon while being transparent about data gaps and situations where the model is less reliable. Describe how you would work with Product and Design to make a predictive insight useful in the application. I would start with the workflow, user decision, and moment at which an insight can change behavior. With Product and Design, I would define the action, explanation, confidence presentation, and feedback mechanism before finalizing the model interface. I would then test whether users understand and act on the insight, measure downstream outcomes, and iterate on both the model and the experience. Opportunity details ## About this role. A little about us…Fleetio is a modern software platform that helps thousands of organizations worldwide manage their fleet operations. Transportation technology is a hot market, and we’re leading the charge with raving fans and new customers signing up every day. We raised $450M in our Series D funding round in March of 2025 and are on an exciting trajectory as a company. Fleetio is also a proud [founding member of the Rails Foundation](https://rubyonrails.org/foundation)! More about our team and company: * Fleetio overview video: [https://www.youtube.com/watch?v=YoXyXTFWbkg](https://www.youtube.com/watch?v=YoXyXTFWbkg) * Our careers page: [https://www.fleetio.com/careers](https://www.fleetio.com/careers) 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. ### More About Our Team and Company * Watch our culture videos: [https://fleet.io/culture](https://fleet.io/culture) * Engineering culture, interview process, and videos: [https://www.fleetio.com/careers/engineering](https://www.fleetio.com/careers/engineering) * Fleetio overview video: [https://www.youtube.com/watch?v=IlvIbwZT3oU](https://www.youtube.com/watch?v=IlvIbwZT3oU) * More about the Fleetio platform: [https://www.fleetio.com/features](https://www.fleetio.com/features) * API docs: [https://developer.fleetio.com](https://developer.fleetio.com) 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. ### 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 #LI-Remote Fleetio provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment. We celebrate diversity and are committed to creating an inclusive environment for all. All employment is decided on the basis of qualifications, merit and business need. This application is not intended to and does not create a contract or offer of employment. Employment with Fleetio is at will. If you have a disability or a special need that requires an accommodation to fill out the online application, please let us know by calling (205) 718-7500. Show more [Apply now >](https://jobicy.com/jobs/155336-senior-applied-data-scientist-fleet-intelligence.md) > Annual salary information is not provided for this position. 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