Senior Data Scientist

About PrePass

PrePass® is North America's most trusted weigh station bypass and toll management platform. We’re transforming how the transportation industry operates—creating solutions that keep trucks moving safely, efficiently, and compliantly. This means making bold decisions and building systems that support not only fleets but the broader economy. It all starts with enabling commercial vehicles to keep rolling with seamless toll management, weigh station bypass, and safety solutions. It’s what we do best, and we do it to meet the demands of the road every day.

That’s why people join us: our solutions are implemented in real-time, on highways and interstates across the nation, helping fleets go farther, faster. This work challenges and rewards, presenting complex problems that need ambitious answers. We hire bold thinkers with a heart for impact, a passion for progress, and the optimism to shape the future of transportation.

About the Role

We’re seeking a Senior Data Scientist to lead advanced analytics and machine learning initiatives that drive strategic decisions and product innovation. You will work cross-functionally with data engineers, BI analysts, product and operational teams to develop scalable models and insights that shape the future of transportation logistics. This role is fully remote.

Key Responsibilities

Data Science and Machine Learning

• Design, train, and evaluate predictive and optimization models using statistical, machine learning, and deep learning techniques.
• Build and deploy models for classification, regression, clustering, and anomaly detection.
• Develop time series forecasting models for churn prediction and demand forecasting.
• Balance R&D prototyping with production-grade model development.

Feature Development and Engineering

• Partner with data architects and engineers to source, clean, and transform large datasets from the Fabric data lakehouse and warehouse environments.
• Create reusable data pipelines and feature stores for model training and scoring.
• Ensure model reproducibility, explainability, and integration into downstream reporting or applications.

Programming and Tools

• Use Python extensively with libraries such as scikit-learn, pandas, numpy, statsmodels, seaborn, and matplotlib.
• Work with SQL and T-SQL for data manipulation and analysis.
• Create compelling visualizations and dashboards using Power BI including DAX measures.

Business Collaboration and Impact

• Partner with BI analysts, data engineers, and architects to understand business needs.
• Translate complex modeling outcomes into actionable business recommendations.
• Present findings and model outcomes to non-technical stakeholders across Product, Operations, and Leadership.
• Contribute to the development of scalable data science infrastructure and best practices.

Qualifications

Required

• Bachelor’s degree in computer science, statistics, mathematics, machine learning, or equivalent quantitative field. (Master’s or PhD preferred.)
• 5+ years of experience in building machine learning models in business contexts.
• Proven track record of deploying models in production environments.
• Strong collaboration and communication skills in dynamic, cross-functional teams.

Preferred

• Proficiency in Microsoft cloud technologies, especially Fabric.
• Experience building ML pipelines with MLOps features including preprocessing, training, model deployment, monitoring/retraining, and CICD.
• Experience working with GPS telemetry data including latency, gaps, and error handling.
• Familiarity with geospatial data science including spatial joins, clustering, ArcGIS GeoAnalytics, and geohashing.