Key skills
PythonMachine LearningMLComputer VisionPyTorchscikit-learnPandasData EngineeringAnalyticsSnowflakeAWSAgile
About this role
GenLogs is a transportation-technology company building the next generation of truck intelligence. The Analytics Engineer will develop and deploy production-grade analytics integrated into GenLogs' core platforms, focusing on machine learning systems and large dataset analysis to uncover actionable patterns.
Responsibilities:
- 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
Requirements:
- 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
- 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)
- Experience with agile development and supporting production deployments, monitoring, and support functions
- Experience working with cloud-based data tooling (Snowflake, AWS)