Biomedical Scientist jobs in United States

Are you passionate about leveraging data to solve complex, real‑world problems?

Our client is looking for a skilled Senior Data Scientist to lead the development of advanced machine learning models and geospatial analytics that power smarter transport and logistics solutions.

You’ll join a collaborative team bridging the UK, US and South Africa, turning rich sensor and geospatial datasets into intelligence that drives meaningful operational improvements for top‑tier clients and major government agencies.

If you have experience with sensor data, spatial data, radar analytics or time series forecasting, we’d love to hear from you!

• Machine learning & data science: Building and validating classification/regression models; applying precision/recall, confusion matrices, and error analysis to drive improvements.
• Python expertise: Strong experience with ML, data processing, and visualisation libraries (scikit‑learn, PyTorch, pandas, matplotlib, plotly, etc.).
• Sensor data handling: Comfortable working with spatial time‑series data (radar point clouds, GPS, UWB, accelerometer, etc.).
• Visualisation & dashboards: Translating outputs into actionable insights via plots, overlays, and interactive dashboards.
• Cloud data engineering: Experience with cloud platforms (Azure, AWS, GCP) for data ingestion, storage, and analytics (e.g., IoT Hub, S3, Pub/Sub, CosmosDB/BigQuery).
• Collaboration & documentation: Effective cross‑disciplinary communication; producing reproducible code, pipelines, and well‑documented results.

• Radar data – exposure to radar sensors and features (RCS, Doppler, track stability) a strong plus.
• Data fusion – fusing multiple sensor data streams (radar, video, GPS, UWB) into coherent datasets.
• Contextual data integration – enriching sensor data with static and dynamic sources (weather, POI, events, population stats).
• Geospatial data – working with maps, coordinate systems, overlays, and joining spatial/temporal datasets.
• Cloud‑native pipelines – deploying ML/data pipelines using serverless functions, containers, and workflow tools (e.g., Azure Functions, AWS Lambda, GCP Dataflow).
• Scalable storage & analytics – handling large datasets with cloud‑based databases, distributed processing, and query engines (e.g., SQL, Spark, Databricks).

If you’re ready to transform geospatial and radar data into meaningful insights that drive smarter, more efficient solutions, please apply!

Join an ambitious, globally connected team where your machine learning expertise will directly shape real‑world outcomes and accelerate innovation in the data science space.