Element Fleet Management is looking to support its model strategy by uncovering correlations between driver behavior and key cost categories such as fuel, maintenance, and accidents to deliver a superior client experience.
Requirements
- Proficient in R studio, SQL and Python for data analysis and manipulation
- Familiar with machine learning libraries such as scikit-learn, XGBoost, or TensorFlow
- Skilled in using Power BI to build interactive dashboards and reports
- Hands-on experience with data manipulation libraries like pandas, NumPy, and dplyr for cleaning and transforming data
- Solid understanding of statistical techniques such as correlation analysis, regression, and clustering
- Exposure to working with large datasets and time-series data is an asset
- Familiarity with Snowflake for cloud-based data storage and analytics
Responsibilities
- Analyze fleet related datasets to identify behavior patterns impacting fleet costs.
- Develop statistical and machine learning models to correlate driver behavior with fuel consumption, maintenance, and accidents.
- Design and implement ETL processes for data ingestion, transformation, and preparation.
- Ensure data quality and model reliability through robust validation techniques.
- Support and maintain existing R Studio / Posit scripts to preserve legacy workflows and ensure smooth integration with modern systems.
- Present complex findings to both technical and non-technical stakeholders.
- Collaborate with Advisors to transform model output into actionable client insights.
Other
- Bachelor's or master's degree in data science, Computer Science, Statistics, Engineering, or a related field required
- 1–3 years of experience in data science, analytics, or data engineering roles
- Strong problem-solving and critical-thinking skills
- Excellent verbal and written communication abilities
- Self-starter with initiative and ability to work independently
