Top Data Science Jobs

Develop, deploy, and monitor predictive models and algorithmic decision systems for automated sports pricing and market making. Prepare data, build backtesting and simulation tools, analyze model and trading performance, and improve quoting, pricing, exposure management, and execution quality. Create dashboards and diagnostics while collaborating with traders, quantitative engineers, product, and engineering teams. The role includes occasional evenings, weekends, and holidays aligned with major sporting events.

Develop algorithms, models, and analytical systems to transform complex structured and unstructured datasets into actionable insights. Collaborate with clients to define questions, gather and clean data, apply statistical and scientific methods, and support decisions across areas such as fraud detection, cancer research, and national intelligence. The role requires R or Python, advanced quantitative skills, and an active TS/SCI clearance with polygraph.

Develop algorithms, models, and analytical systems to transform structured and unstructured data into actionable insights. Collaborate with clients to define analytical needs, gather and cleanse data, apply scientific methods, and support optimization, simulation, prediction, and decision analysis. The role requires R or Python, statistical or mathematical experience, large-scale data analysis capabilities, and a TS/SCI clearance with polygraph.

Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.

Leads applied data science initiatives involving Python, NLP, LLMs, and end-to-end machine learning workflows. Explores, cleans, analyzes, visualizes, and mines large-scale data; designs models for document classification, extraction, summarization, and search; and deploys solutions in production streaming and data lake environments. Collaborates with clients, MLOps, and infrastructure teams while guiding teammates and supporting model deployment, monitoring, and retraining.

Develops algorithms, models, and analytical systems to transform large structured and unstructured datasets into actionable insights. Works with clients to define data questions, applies scientific methods, analyzes data, and supports decision-making across areas such as fraud detection, cancer research, and national intelligence. Requires R or Python, statistical or operations research experience, and a TS/SCI clearance with polygraph.

Leads intelligence-focused analytics product development, integrates structured and unstructured data, performs quantitative and qualitative analysis, and provides subject matter expertise during command exercises. The role requires data science experience, model and dashboard development, data transformation, visual analytics, and structured report writing. Experience with a U.S. military functional or geographic command is preferred. The position is based in Honolulu and requires an active Secret clearance with eligibility for Top Secret.

Supports the design, development, testing, and deployment of AI and machine learning solutions for business applications including forecasting, manufacturing optimization, demand planning, and automated decision-making. Builds prototypes, evaluates AI/ML frameworks, works with large datasets, documents best practices, monitors solution performance, researches emerging techniques, and communicates results to technical and non-technical stakeholders.

Build AI, machine-learning, and statistical models to predict clinical-trial outcomes, regulatory results, and commercial potential. Analyze trial designs, endpoints, patient populations, treatment effects, safety, and subgroup data. Apply generative AI, LLMs, and NLP to scientific and regulatory information. Develop pipeline-monitoring tools, validate models, control bias and data leakage, and translate clinical and quantitative findings into investment conclusions supporting equity research, forecasts, valuations, and published analysis.

Develop data science and AI/ML solutions for Federal clients using Python, SQL, LLMs, Tableau, Databricks, and cloud platforms. Build data pipelines, wrangle data, deploy models, automate processes, and translate technical findings for varied audiences. Engage clients to gather requirements, support mission initiatives, contribute to business development materials, and help deliver analytics products in remote, hybrid, or on-site Washington, D.C.-area environments.

Develop descriptive, predictive, and causal analytics for local government datasets. Responsibilities include maintaining data pipelines, cleaning and refreshing databases, weighting surveys, building statistical and machine learning models, conducting impact studies, automating cloud workflows, presenting insights, and documenting reproducible processes. The role partners with engineering, marketing, sales, customer success, and survey research teams, and requires Python, SQL, statistics, communication, and applied data science experience.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

Build production data pipelines, reliability models, scoring systems, analytics tools, APIs, and LLM-powered workflows for Starlink hardware reliability. Analyze telemetry, manufacturing, inventory, field-return, support, and image data; deploy and monitor machine learning models; and integrate outputs into replacement, support, production, quality, and supply-chain decisions. The role requires cross-functional collaboration and on-site work in Bastrop, Texas, with up to 10% travel and occasional extended hours.

Develop and deploy production-grade AI and data science solutions for scientific discovery, including LLM applications, NLP, semantic search, RAG, agentic workflows, knowledge graphs, and recommendation systems. Lead model evaluation, experimentation, monitoring, and lifecycle management while collaborating with engineering, product, research, and domain teams. Influence technical strategy, mentor data scientists, and deliver reliable, scalable, evidence-grounded AI products.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

The Data Scientist develops advanced analytics, AI, machine learning, predictive models, and business intelligence solutions supporting pharmaceutical commercial strategies. Responsibilities include analyzing complex healthcare and longitudinal patient data, building scalable algorithms and automated analytics capabilities, integrating and governing cloud data, and communicating insights to technical and non-technical stakeholders. The role partners cross-functionally to support market intelligence, customer segmentation, targeting, promotional measurement, and data-driven decision-making across oncology, rare disease, and CNS franchises.

Build and deploy data science models for demand and sales forecasting, promotion effectiveness, pricing, and retail analytics. Analyze messy datasets from sell-through, inventory, deductions, trade promotions, and supply-chain signals. Develop statistical quality checks, evaluate model performance, quantify uncertainty, and collaborate with AI/ML engineers to ship production-grade insights within the product.

Develop machine learning, statistical, graph-based, and entity-resolution algorithms for large-scale identity verification and deceased monitoring. Build Spark/PySpark and AWS data pipelines, engineer features, analyze data quality, conduct experiments, and support model evaluation. Write Python/R and SQL for extraction and validation, investigate compliance data, create dashboards, and communicate findings to cross-functional teams.

Develop and improve analytical and machine learning models supporting product quality, risk outcomes, and business decisions. Responsibilities include statistical analysis, feature development, data exploration, predictive modeling, SQL-based analysis of large datasets, algorithm research, and workflow documentation. The role collaborates with data scientists, product managers, data engineers, and analysts in a product-focused fintech environment.

Analyze and clean structured and unstructured data, assess data sources, build statistical and AI/ML models, and create Tableau visualizations and dashboards. The role develops actionable insights for national security decision-making, communicates findings through reports and briefings, researches analytic methods, responds to complex ad hoc analysis requests, and provides technical education to staff. The position is contingent upon contract award and requires an active Top Secret clearance.

Develop and evaluate advertising auction and monetization models, revenue and inventory forecasts, product KPIs, and rigorous A/B testing programs. Analyze customer and advertising data to identify trends, explain test outcomes, optimize revenue, and guide product strategy. Communicate concise, compelling insights through fact-based reporting and presentations while supporting large-scale, user-facing advertising products.

Develops statistical and machine-learning models for pricing strategy, revenue optimization, demand forecasting, segmentation, and utilization analysis. Designs experiments, validates and documents models, prepares large-scale datasets, and partners with data engineering teams on scalable pipelines and deployment. Translates analytical outputs into pricing recommendations, dashboards, and decision tools while supporting governance, stakeholder presentations, and analyst enablement.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Leads enterprise data science and AI/ML strategy for a federal modernization program. Designs, deploys, monitors, and scales machine learning, advanced analytics, MLOps, streaming pipelines, and responsible AI governance frameworks. Integrates analytics into complex enterprise systems, supports cloud-native and DevSecOps delivery, ensures security and compliance, presents technical recommendations, improves data quality, and mentors data science teams.

Principal Data Scientist supporting a U.S. Government customer by designing and implementing machine learning, data mining, statistical analysis, AI, and advanced analytics solutions. The role requires analyzing complex datasets, developing predictive and prescriptive models, using Python or R and database technologies, and providing technical expertise for mission-critical software and systems. It is based in Annapolis Junction with limited partial telework and requires an active or reinstatable TS/SCI with Polygraph clearance.