Senior Applied Data Scientist, Fleet Intelligence

This is a fully remote position, open to applicants in United States, +2 more countries.

📋 Description

• Implement near-term Fleet Intelligence projects, such as tire intelligence, utilization intelligence, ROI assessment, existing predictive models, and analytical foundations for customer-focused insights.

• Assess and create forecasts or predictive models concerning fleet usage, maintenance expenses, availability, condition, failure risks, and asset lifecycle decisions.

• Convert product inquiries into hypotheses, target variables, baselines, evaluation strategies, and incremental delivery milestones.

• Investigate maintenance, usage, cost, work-order, telematics, warranty, and asset-history data to uncover predictive signals and identify data gaps.

• Develop, validate, and operationalize models from experimentation through to production monitoring and iterative improvement.

• Establish metrics for model quality, confidence thresholds, drift detection, and feedback mechanisms.

• Collaborate with Product and Design teams to ensure model outputs are understandable, explainable, and actionable within customer workflows.

• Create reusable practices for experimentation, model documentation, validation, monitoring, and responsible claims regarding predictive performance.

• Present findings, trade-offs, risks, and recommendations to technical partners, product leaders, and executives.

• Disseminate knowledge through design reviews, documentation, collaboration, and mentorship.

⛳️ Requirements

• Over 5 years of experience in applied data science, machine learning, statistical modeling, or a closely related field.

• Proven track record of developing and deploying models or decision-support systems that impacted actual customer or business results.

• Strong expertise in Python and SQL, including exploratory analysis, feature engineering, model development, and evaluation on large datasets.

• Solid foundation in statistics and machine learning principles, encompassing 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 a few areas is prioritized over breadth in all.

• Familiarity with transitioning models from notebooks to dependable production workflows, including version control, testing, deployment, observability, performance tracking, and retraining or refresh strategies.

• Experience using contemporary cloud data platforms and transformation workflows such as Snowflake, dbt, and orchestration tools.

• Ability to recognize data quality limitations, suggest improvements, and work alongside data engineers on pipelines and source reliability.

• Exceptional written and verbal communication skills, particularly in conveying complex methodologies, uncertainties, and trade-offs to non-specialists.

• Experience collaborating cross-functionally with Product, Design, Software Engineering, and Data Engineering teams.

🏝️ Benefits

• Various health/dental coverage options (100% coverage for employee, 50% for family).

• Vision insurance.

• Incentive stock options.

• 401(k) match of 4%.

• Paid time off - 4 weeks (increases after the second year!).

• 12 company holidays plus 2 floating holidays.

• Parental leave - 16 weeks paid for birthing parent and 4 weeks paid for non-birthing parent.

• Flexible Spending Account (FSA) & Health Savings Account (HSA) options.

• Short and long-term disability coverage (short-term 100% paid).

• Community service funding.

• Professional development funding.

• Wellbeing fund - $150 quarterly.

• Business expense allowance - $125 quarterly.

• Mac laptop and new hire equipment stipend.

• Fully stocked kitchen with a variety of drinks and snacks (BHM only).

• Remote work-friendly since 2012.