Top Data Science Jobs

Supports federal enterprise analytics by preparing structured and unstructured data, developing and evaluating machine learning and AI models, applying NLP for entity extraction, creating data visualizations, documenting results, and participating in Agile ceremonies. The role requires collaboration with senior data scientists and supports an active federal contract at DEA facilities in Virginia.

Analyze and prepare data using SQL and Python, investigate data quality, apply statistical methods, and support predictive modeling. Help develop and validate AI-enabled analytics tools, including MCP integrations, while testing AI-generated outputs against trusted data sources. Collaborate with stakeholders to create analyses, visualizations, repeatable workflows, and practical business solutions. Communicate findings clearly, support data governance and user adoption, and contribute to analytics projects across NASCAR.

Develop and deploy data science models and systems using structured and unstructured data. Analyze, cleanse, and visualize data; apply supervised and unsupervised machine learning, inferential statistics, and research design principles. Build dashboards and lightweight web applications, integrate systems, advise clients, and guide teammates in delivering data-driven solutions. The role requires a bachelor's degree and an active TS/SCI clearance with polygraph.

Define product, feature, and agent metrics; design and analyze experiments; evaluate model and product performance; investigate anomalies and regressions; build statistical models, dashboards, and analytical tools; and translate complex analyses into recommendations for Product, Engineering, and Machine Learning teams. The role focuses on connecting offline AI evaluations with production outcomes and customer impact.

Lead FX quantitative research within Treasury Finance Data Science. Develop exposure forecasting, VaR, Expected Shortfall, stress testing, hedge optimization, machine learning, and statistical risk systems. Build production-grade decision systems with Engineering, ensure monitoring and reliability, and provide leadership reporting. Partner with Treasury and FX stakeholders to shape risk strategy, mentor data scientists, and translate complex quantitative analyses into actionable decisions.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Supports federal enterprise data analytics by developing, training, and evaluating machine learning and AI models; applying NLP to unstructured data; preparing and cleansing structured and unstructured datasets; expanding entity extraction; documenting model results; creating data visualizations; and participating in Agile ceremonies.

Develops and validates machine learning and AI models, applies NLP to structured and unstructured data, performs entity extraction and enrichment, and builds ETL processes and visualizations. Responsibilities include model monitoring, bias detection, retraining, API and pipeline deployment, documentation, and collaboration within an Agile federal analytics program supporting DEA mission objectives.

Leads predictive modeling and advanced analytics projects using large-scale distributed data. Develops and improves proprietary algorithms, batch and real-time scoring approaches, visualizations, and actionable business insights. Collaborates with business partners on segmentation and quality, utilization, and cost-related patterns. Publishes findings, addresses limitations, ensures targets are met, and mentors other data scientists.

Own the end-to-end data science lifecycle for payment optimization, including feature generation, model development, tuning, validation, experimentation, and deployment. Build experimentation frameworks to measure conversion, declines, fraud, and merchant impact. Collaborate with data engineering, BI, infrastructure, and product teams to develop scalable pipelines and models. This role establishes DEUNA’s data science practice and may develop into people leadership.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, analyses, reports, and experiments; surface insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings clearly, and driving measurable business outcomes.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires strong SQL or Python skills, independent analytical problem-solving, ownership of ambiguous initiatives, and persuasive communication with leadership.

Develops statistical and machine learning solutions for defense intelligence analysis. Responsibilities include collecting, cleansing, and analyzing structured and unstructured data; programming in Python and SQL; creating Tableau dashboards and reports; applying NLP methods; operationalizing AWS-based data sources; and communicating analytical findings. The role also supports business intelligence reporting, independently solves moderately complex problems, and may guide junior employees.

Own end-to-end analytical work for a business unit across growth marketing, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, conduct analyses, design experiments, and communicate insights and recommendations to stakeholders. The role requires independently structuring ambiguous problems, using SQL or Python, influencing leadership decisions, and driving measurable business outcomes.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; uncover insights; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data skills, persuasive communication, and ownership of ambiguous problems using SQL or Python.

Build and own a marketing measurement and optimization system for paid acquisition. Develop Bayesian marketing mix models, causal incrementality experiments, response curves, LTV and retention models, and risk-aware budget allocation methods. Translate model outputs into profitable channel decisions, production-quality code, and actionable recommendations. Partner with paid acquisition and data engineering teams to establish testing roadmaps and measurement approaches for both established and opaque advertising channels.

Analyze digital customer behavior using SQL, Python, and PySpark to generate actionable business insights. Build automated analytics workflows, Power BI dashboards, data visualizations, and customer-level models. Partner with Digital, IT, Decision Science, and external stakeholders to interpret large datasets, communicate findings to nontechnical audiences, and support digital engagement, coupon, cashback, and loyalty initiatives.

Provide data science and analytical support for evaluating, modernizing, and optimizing complex enterprise systems. Collect, process, analyze, and visualize operational and technical data; develop metrics, dashboards, models, reports, and automated processes; investigate anomalies and performance issues; assess data quality; and communicate recommendations to government stakeholders. Collaborate with engineering, software, architecture, cybersecurity, and mission teams to support data-driven government decision-making and operational planning.

Develops advanced data science and machine learning solutions for cybersecurity and intelligence missions. Responsibilities include statistical analysis, predictive modeling, entity resolution, graph analytics, data integration, model evaluation, analytical application development, interactive visualization, and transitioning prototypes into production. The role collaborates with software, data, and platform engineers, translates mission requirements into scalable solutions, improves data interoperability, and provides technical guidance. Work is full-time onsite in Annapolis Junction, Maryland, supporting classified defense intelligence programs.

Develop data science solutions for complex structured and unstructured datasets. Responsibilities include data exploration, cleaning, pipeline development, statistical analysis, visualization, algorithm development, machine learning, NLP, and client advising. The role also involves leading solution development and guiding teammates while using tools such as Databricks, visualization platforms, and programming languages to generate actionable insights.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Leads end-to-end data science and machine learning initiatives, including predictive modeling, statistical analysis, healthcare data extraction, and production deployment. Partners with business and technical leaders to develop data science roadmaps, establishes modeling and analytics standards, evaluates emerging technologies, and mentors data science professionals. The role requires strong programming, SQL, MLOps, stakeholder communication, and advanced analytics expertise.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines across lending products. Analyze large transactional datasets using Python and SQL, partner with Fraud Operations to refine models, reduce false positives and negatives, improve underwriting and verification, apply AI in production, and lead and mentor team members.