Senior Applied Data Scientist, Fleet Intelligence

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.

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.