We're looking for a Data Scientist to build analytical and machine learning solutions that turn large-scale geospatial and mobility data into actionable insights. You'll work closely with data engineers and product stakeholders to detect anomalies, model behavior, and support operational and strategic decisionsshipping/maritime experience is a plus, but not required.
Responsibilities
- Analyze high-volume movement data (trajectories, operational events) to uncover trends, patterns, and anomalies
- Build geospatial analytics: geofencing, entry/exit & dwell time, proximity detection, clustering, hotspot/density mapping
- Translate real-world movement into measurable features (speed/heading profiles, stop patterns, segmentation)
- Develop models for key use cases such as anomaly detection, event classification (e.g., anchoring/berthing), risk scoring, and behavioral segmentation
- Define success metrics and run experiments to validate hypotheses and quantify impact
- Partner with engineering to productionize pipelines (batch and, where needed, near real-time scoring) and monitor model/data quality (drift, performance degradation)
- Communicate results via dashboards and decision-ready reporting; document methods and assumptions
Qualifications
- Bachelor's degree in Data Science/Computer Science/Stats/Engineering (or equivalent experience)
- 5+ years in data science/analytics working with messy, real-world data
- Strong Python (pandas/numpy/scikit-learn) and advanced SQL (BigQuery/PostgreSQL)
- Solid foundation in statistics, experiment design, feature engineering, and model evaluation
- Practical geospatial experience (spatial joins, proximity analysis, clustering, coordinate handling)
- Familiarity with geospatial tools such as PostGIS, GeoPandas, Shapely, H3/geohash
- Experienced in time-series modeling / behavior segmentation; anomaly detection in production
- Exposure to AIS/SAR/EO or similar high-frequency sensor/location datasets is an advantage
- Experience in PyTorch is preferred
- Streaming/near real-time analytics concepts (Kafka/Flink, event time vs processing time)
What We Look For
We are looking for individuals which are growth-minded with high learning agility. We also value data scientists who can think critically about model assumptions, causal inference, uncertainty, and experimental design, ensuring that solutions are not only accurate but also explainable and reliable in real-world maritime settings. The working environment is a startup setup, so expect things to move very fast.
Apply Now.
Visit us at https://lync-lab.com/
