Senior Applied Data Scientist, Fleet Intelligence

• Deliver near-term Fleet Intelligence initiatives, including tire intelligence, utilization intelligence, ROI measurement, existing predictive models, and analytical foundations for customer-facing intelligence




• Evaluate and develop projections or predictive models for fleet usage, maintenance cost, availability, condition and failure risk, and asset lifecycle decisions




• Translate product questions into hypotheses, target variables, baselines, evaluation plans, and incremental delivery milestones




• Explore maintenance, usage, cost, work-order, telematics, warranty, and asset-history data to identify predictive signals and 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




• 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 predictive-performance claims




• Communicate findings, tradeoffs, risks, and recommendations to technical partners, product leaders, and executives




• Share knowledge through design reviews, documentation, pairing, and mentorship

Requirements

• 5+ years of experience in applied data science, machine learning, statistical modeling, or a closely related role




• 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 areas more important than breadth across all




• 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




• 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

Core Competencies

Demonstrates expertise in applied data science and machine learning, with a strong focus on model development, evaluation, and operationalization. Proficient in Python and SQL, capable of translating complex data insights into actionable strategies for fleet intelligence and predictive analytics.

Highest-signal resume keywords

• Applied Data Science




• Machine Learning




• Python Proficiency




• SQL Proficiency




• Predictive Modeling

ATS Optimization Keywords

Hard Skills

• Statistical Modeling




• Feature Engineering




• Time-Series Forecasting




• Regression Analysis




• Classification




• Anomaly Detection




• Model Validation




• Performance Monitoring




• Data Quality Assessment




• Exploratory Data Analysis

Soft Skills

• Excellent Communication




• Cross-Functional Collaboration




• Mentorship

Industry Keywords

• Fleet Intelligence




• Predictive Models




• Data Engineering




• Customer Workflows




• Model Documentation

Tools & Technologies

• Snowflake




• Dbt




• Orchestration Tools