Analyze large volumes of structured and unstructured data from field deployments to uncover trends, anomalies, and actionable insights for customers.
Develop scalable analytics pipelines to support customer-facing dashboards, reports, and intelligence products.
Collaborate with product and customer success teams to frame high-value business and operational questions and translate them into data science workflows.
Identify performance gaps, failure modes, and drift in edge-deployed models by analyzing historical outputs, sensor metadata, and ground-truth comparisons.
Partner with the modeling team to design feedback mechanisms for continuous learning, dataset enrichment, and model retraining.
Build tools and internal services for data visualization, metric tracking, and experimentation across field data.
Contribute to the design and refinement of metrics for evaluating perception, detection, and fusion performance across time and space.
Ensure data quality and integrity across the pipeline, including logging validation, schema enforcement, and anomaly detection.
Stay current with best practices in large-scale data analytics, monitoring, and applied ML, and advocate for their integration into team workflows.

Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field
3–5 years of experience in applied data science, with a track record of translating raw data into production insights or tools.
Proficiency in Python and common data science libraries (e.g., pandas, numpy, scikit-learn, matplotlib/seaborn, SQL).
Experience working with time-series, geospatial, or multi-sensor data in production environments.
Strong analytical thinking and statistical modeling skills, including clustering, regression, and anomaly detection
Familiarity with ML operations concepts like dataset versioning, data labeling workflows, and model monitoring
Excellent communication skills for presenting complex insights to both technical and non-technical stakeholders
Bonus: experience supporting or analyzing ML systems at the edge, or in environments like maritime, automotive, or aerospace domains.

Staff Data Scientist

Key skills