Senior Principal Data Scientist

Description

The company is a wholly owned subsidiary of a larger IT and intelligence services provider. It builds agile IT and intelligence solutions that help organizations and end users work faster and more securely in changing environments.

The role sits in Crystal City, VA, within a fast-paced, multidisciplinary team. It supports the Department of War, the largest US government agency, in its mission to keep the country safe and secure.

You will work with large, complex, and varied datasets to build practical analytic methods, find meaningful patterns and links, and turn technical findings into capabilities that meet critical customer needs.

Day to day, you will turn large, complex data into clear, actionable insights. You will work alongside analysts, software developers, and technical specialists to solve hard data problems and move successful methods from exploration into repeatable operational use.

Responsibilities

• Run structured, repeatable analysis across large, varied datasets to surface patterns, trends, anomalies, relationships, and other signals.
• Explore unfamiliar datasets by checking quality, completeness, distributions, and relationships, and by noting their limits, so you can pick the right approach.
• Build and maintain analytic tools such as queries, scripts, notebooks, lightweight services, and reusable code that automate recurring work.
• Design and test statistical, machine learning, and algorithmic methods for classification, clustering, anomaly detection, similarity, prioritization, entity resolution, relationship discovery, and prediction.
• Set validation methods, benchmarks, scoring, thresholds, and confidence measures, and explain the strengths and limits of each result.
• Build and improve interactive dashboards and lightweight GUIs, such as Streamlit apps, for data exploration, link review, analyst workflows, model evaluation, and structured outputs.
• Produce analyst-ready tables, visuals, summaries, briefings, and structured exports that give decision-makers clear direction.
• Work with analysts and engineers to turn ad hoc analyses and working prototypes into reusable pipelines, standard methods, documented workflows, and maintainable capabilities.
• Help integrate, test, and refine analytic methods in operational environments, keeping outputs reproducible, explainable, and usable by both technical and non-technical readers.
• Document assumptions, methods, data transformations, validation steps, and known limitations to support reproducibility and peer review.

Requirements

• Strong Python skills for analytic work, including data processing, automation, exploration, visualization, algorithm development, and reproducible scripts or notebooks, using pandas, NumPy, SciPy, scikit-learn, or similar tools.
• Strong SQL and hands-on work with large datasets on platforms such as Trino, PostgreSQL, Hive, OpenSearch, or Elasticsearch. Experience with distributed query or large-scale data environments is strongly preferred.
• Experience applying statistical, machine learning, or algorithmic techniques to real data, choosing methods to fit the data and the operational problem rather than relying only on predefined models.
• Experience building repeatable analytic workflows, covering source triage, exploratory analysis, profiling, quality checks, transformations, feature development, validation, documentation, and reusable code.
• Practical experience developing methods for entity resolution, relationship discovery, and graph or relational analysis, including scoring, thresholds, validation checks, and confidence measures.
• Experience evaluating methods with suitable metrics, baselines, test datasets, and sensitivity analysis, and identifying sources of error, uncertainty, or degraded performance.
• Experience integrating structured, semi-structured, and text data by extracting key fields, normalizing across sources, resolving inconsistencies, and combining schemas.
• Ability to investigate complex or poorly defined problems on your own, learn unfamiliar datasets quickly, test approaches, and refine them based on results and stakeholder feedback.
• Ability to turn analysis into usable outputs for analysts and decision-makers, including clear visuals, concise narratives, dashboards, structured deliverables, and explanations of confidence and limitations.
• Strong communication and collaboration skills across multidisciplinary teams that include analysts, data scientists, data engineers, software engineers, and mission stakeholders.
• 10+ years of related experience.
• Bachelor's degree in computer science, statistics, engineering, or a related field, or equivalent experience. Advanced degrees are a plus.
• Skills in data analytics, mathematical modeling, Python, statistical analysis, and SQL.
• Desired: experience with applied machine learning such as classification, clustering, dimensionality reduction, anomaly detection, ranking, similarity analysis, natural language processing, or time-series analysis.
• Desired: experience with graph data and analytics, including knowledge graphs, network analysis, graph databases, and community detection across connected data.
• Desired: experience with geospatial, temporal, or other specialized data, including spatial or time-dependent relationships in analytic workflows.
• Desired: familiarity with modern AI and large language models for information extraction, classification, summarization, entity identification, and other parts of analytic workflows.
• Desired: experience building or integrating APIs and lightweight Python services, such as FastAPI, to expose analytic methods to downstream applications and users.
• Desired: familiarity with deploying applications in Docker and using CI/CD pipelines for repeatable, secure, maintainable delivery.
• Desired: familiarity with Git best practices, including branching, pull requests, code reviews, merge conflict resolution, and clean, well-documented repositories.
• Desired: experience connecting React frontends to RESTful APIs and Python backend services.
• Desired: familiarity with large-scale data tools such as Apache Iceberg, Spark, object storage platforms, or similar architectures.
• Desired: experience moving data science work from research or prototyping into reliable, maintainable operational tools in multidisciplinary settings.

Conditions

Regular position, onsite in Crystal City, VA, with 40 scheduled hours per week.

Role requirements include travel for 60-90 days per year to CONUS sites. The posting's travel field lists none.

Current Top Secret/SCI clearance is required, along with the ability to obtain a clearance requiring a CI polygraph. Public trust clearance is not required.

Only US citizens are eligible, due to US government contract requirements.

No certifications are required.

The likely salary range is $187,000 - $253,000. This is not a guarantee. Pay depends on experience, geographic location, and possibly contract requirements, and may fall outside this range.

As part of hiring, you will complete an identity verification process using biometrics and AI. You must be on camera during virtual interviews, and your biometric data will be used for verification and security.

The company is an equal opportunity employer that welcomes individuals with disabilities and protected veterans.

Benefits

• Medical plan options, some with Health Savings Accounts, plus dental and vision plans.
• 401(k) plan with pre-tax and post-tax contributions up to IRS annual limits and a company match.
• Full flex work weeks where possible, and a range of paid time off, including vacation, sick and personal time, holidays, paid parental, military, bereavement, and jury duty leave.
• Short and long-term disability, life, accidental death and dismemberment, personal accident, critical illness, and business travel and accident insurance, provided or available.
• Competitive pay, an AI-powered career tool that suggests career steps and learning, and an internal mobility team to help you reach your goals.
• A military-friendly workplace.