Staff Data Scientist

Quartermaster•Washington, DC

1d•Remote

About The Position

We are seeking a Staff Data Scientist to help us turn vast, high-dimensional sensor data into actionable insights for our customers—and smarter models for our edge-deployed systems. This role sits at the intersection of data analytics, machine learning, and product development. You’ll analyze aggregate data collected across fleets and deployments to extract meaningful patterns, trends, and outliers that support operational intelligence and system-wide performance improvements. Just as critically, you’ll work closely with our machine perception and modeling teams to create feedback loops that inform model retraining, edge-case detection, and system calibration. This role is ideal for someone who thrives in full-cycle data science work—from framing a question to deploying a data product.

Requirements

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

Nice To Haves

Bonus: experience supporting or analyzing ML systems at the edge, or in environments like maritime, automotive, or aerospace domains

Responsibilities

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