ABOUT THE DATA TEAM
The Data Science team at GenLogs transforms raw observational data from the Trident sensor network into high-value intelligence used by law-enforcement agencies, regulators, ports, and private-sector freight operators. We build models, analytics, and measurement frameworks that enable vehicle detection, entity resolution, behavioral insights, fraud and theft indicators, compliance signals, and network-wide operational performance metrics. Our work sits at the center of the freight intelligence platform, shaping how billions of roadside observations become actionable information. We partner closely with Engineering and Product to deploy algorithms at scale and with Go-to-Market teams to define customer-facing analyses that drive real operational outcomes. The team blends statistical rigor, ML capability, and domain expertise to create a new standard for freight intelligence in the United States.
ABOUT THE JOB
You’re a problem solver at heart. You thrive at the intersection of engineering, math, and machine learning, and you’re motivated by questions that don’t have obvious answers. You bring a background in engineering, computer science, physics, applied math, or another hard science discipline, and you enjoy applying that technical foundation to real-world ML challenges.
You are energized by ambiguity, obsessed with understanding how complex systems behave, and capable of breaking down big problems into tractable iterations. You ask great questions, validate assumptions with data, and are relentless in your pursuit of signal over noise.
WHAT YOU’LL DO
- Build machine-learning systems that power some of the most advanced logistics intelligence products in the industry
- Analyze large, noisy datasets from cameras, OCR, detections, and geospatial pipelines to uncover actionable patterns
- Design and evaluate algorithms for truck re-identification, geospatial clustering, equipment classification, OCR text labeling, anomaly detection, and more
- Collaborate with engineering and data engineering teams to scale models from prototype to production
- Work closely with product teams to deeply understand customer needs and translate them into modeling and analytics initiatives
- Apply scientific thinking to continuously test, iterate, and refine approaches as new data becomes available
REQUIRED QUALIFICATIONS
- 2–5 years of professional experience in Data Science, Machine Learning, or Software Engineering
- Technical foundation in engineering, physics, math, computer science, or related applied fields
- Experience deploying or building models using:
- Machine learning fundamentals (classification, clustering, time-series, anomaly detection)
- Computer vision (OCR, object detection, embeddings)
- Geospatial data analysis (mapping, clustering, location intelligence)
- Association/sequence pattern mining, feature engineering, or algorithm development
- Experience working with cloud-based data tooling (Snowflake, AWS) — not required but nice to have
- Strong programming skills in Python and comfort with modern data/ML libraries (PyTorch, Pandas, Scikit-learn, etc.)
- Comfort working with real-world messy datasets (sensor data, imagery, telematics, transactional freight data)