Top Data Science Jobs

Develops data science solutions by exploring, cleaning, analyzing, visualizing, and modeling structured and unstructured data. Works with clients to define analytical needs, develops algorithms and machine learning systems, leads complex solution development, and translates data into actionable insights. Potential applications include fraud detection, cancer research, and national intelligence. The role may involve NLP, text mining, AI engineering, LLMs, distributed computing, and production machine learning pipelines.

Analyzes structured and unstructured financial and ERP data using Python, SAS, statistical methods, predictive modeling, and advanced algorithms. Supports Oracle E-Business Suite financial processes, data conversion, reporting, and continuity of operations planning. Develops models and actionable insights for business decisions, collaborates with engineering and disaster recovery teams, maintains contingency plans, and executes recovery exercises. Requires a master’s degree, 5+ years of experience, Oracle expertise, certification eligibility, and approximately 25% annual travel to Washington, DC.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Conduct CBRN operational impact analyses, evaluate scientific test results, develop hazard prediction and sortie-generation models, and create custom software and visualization tools. Support Air Force policy, doctrine, research, and strategic planning through technical reports, recommendations, briefings, and collaboration with defense, academic, and research communities.

Analyze and visualize complex RF, ELINT, and Technical SIGINT datasets supporting Advanced Weapons and Counter Proliferation missions. Develop data workflows, actionable intelligence products, visualizations, and analytical methods while validating data quality and documenting findings. Collaborate with engineers, analysts, and mission partners on RF technology identification, mission planning, and technical intelligence analysis.

Develop data models, algorithms, visualizations, workflows, and systems engineering documentation for an intelligence customer’s AI and risk management capabilities. Integrate and organize structured and unstructured data, validate requirements and controls, support cloud infrastructure in high-security environments, and translate business needs into technical solutions. Collaborate across sponsor organizations, conduct link analysis, identify technology gaps, and communicate technical findings through briefings and reports.

Develop and deploy AI and data-driven systems for reactor servicing, focusing on RAG, LLMs, NLP, safety documentation, and operational procedures. Build Plotly Dash interfaces, optimize Python workflows, package and containerize applications, and collaborate with engineering and IT teams on reliable, compliant AI solutions. The role requires an active DOE L or DoD Secret clearance and supports up to 10% telework.

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Support Computer Network Operations by applying AI and machine learning to network exploitation tools, data, and workflows. Evaluate large language models, analyze mission data using statistical methods, develop Python-based analytic and ML workflows, normalize and visualize data, and collaborate with operators, developers, and engineers to deliver mission-focused technical solutions.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Analyze complex datasets to identify trends, risks, and opportunities; translate findings into business recommendations; present models and insights to senior leadership; collaborate with stakeholders; and develop analytical models, dashboards, reporting tools, and forecasting methodologies.

Develops and manages predictive marketing models, including propensity, cross-sell, upsell, churn, and attribution models. Oversees data sources, model validation, recalibration, MLOps governance, monitoring, and lifecycle management. Delivers marketing analytics projects, improves workflows, applies emerging AI tools, and communicates data-driven recommendations to nontechnical stakeholders. Requires strong statistical modeling, programming, SQL, visualization, cloud data, and cross-functional decision-making skills.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develops end-to-end data science and AI solutions for supply chain and service operations. Responsibilities include building machine learning, GenAI, LLM, agentic AI, RAG, forecasting, and deep learning applications; deploying and monitoring models in production; designing GenAI architectures; analyzing operational data; partnering with stakeholders to improve forecasts and business decisions; and communicating insights to leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Analyze large customer datasets, evaluate machine-learning models, develop risk and fraud insights, and communicate data-driven recommendations to technical and nontechnical audiences. The role partners with sales and account teams throughout pre- and post-sales, creates automated analytical workflows, supports product optimization and upsell opportunities, and may build exploratory ML models. It also contributes thought leadership on identity verification, fraud prevention, model performance, and customer risk management.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Supports the U.S. Space Force by analyzing model-based systems engineering data, extracting and transforming structured and semi-structured information, and developing scripts, dashboards, visualizations, reports, and data collection frameworks. The role requires Python, Java, SQL, and dashboarding experience, collaboration with systems engineers, on-site work in El Segundo, and an active TS/SCI clearance with SAP eligibility.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Lead growth analytics across acquisition, activation, approval, signup, and retention. Partner with product, design, engineering, and marketing leaders to define metrics, size opportunities, prioritize roadmaps, analyze experiments, and deliver strategic recommendations. Build funnel, cohort, and retention analyses; collaborate on self-service metrics; and communicate evidence-based findings to technical teams and executives.

Conduct data science and Multi-INT analytics across complex intelligence datasets. Develop data pipelines, statistical analyses, predictive and machine learning models, AI-enabled applications, dashboards, and visualizations using R, Python, SQL, Power BI, and Tableau. Integrate structured and unstructured data, evaluate model performance, identify trends and anomalies, and communicate analytical findings to technical and mission stakeholders. The position requires a TS/SCI clearance and may involve deployment for up to six months annually.

Build scalable cybersecurity data pipelines and analytic workflows supporting AI-enabled threat detection, anomaly detection, automated triage, and real-time Cyber Fusion Center operations. Use Python, SQL, Apache Spark, Databricks, ETL, and version control to deliver production-ready solutions. Collaborate with cyber operators and AI engineers to prioritize datasets, shape requirements, and enable autonomous cyber defense capabilities.

The Data Scientist will turn messy media and audience data into business insights and decisions. Responsibilities include sourcing and cleaning data, analyzing trends and audience behavior, designing experiments, building and deploying forecasting and content-analysis models on AWS, maintaining trusted dashboards and reports, and applying LLMs and agentic coding tools. The role works autonomously with engineering, product, business, and company leadership in a fast-paced remote startup environment.