Top Data Science Jobs

Develop and implement data science models and algorithms for payments risk management. Analyze complex datasets, ensure data quality and integrity, optimize data processes, and perform advanced statistical analysis and reporting. Collaborate with stakeholders and cross-functional teams to understand data requirements and deliver effective analytical solutions.

Develop and implement data science models and algorithms for payments risk management. Analyze complex datasets, perform advanced statistical analysis, ensure data quality, optimize data processes, and deliver reporting and solutions aligned with stakeholder requirements. The role collaborates cross-functionally to support business needs and improve data efficiency and performance.

Lead end-to-end development and validation of statistical and machine learning models for media measurement. Analyze large datasets, conduct advanced statistical research, collaborate with engineering and product teams on scalable model deployment, and communicate insights through visualizations and presentations. The role requires strong expertise in statistical methods, survey design, sampling, predictive modeling, machine learning, and Python, with SQL and Spark experience preferred.

Lead end-to-end research and development of statistical and machine learning models for media measurement. Analyze large datasets, develop and validate models, collaborate with engineering and product teams, and translate complex findings into visualizations and recommendations for technical and business stakeholders.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Supports business strategy through statistical analysis, experimentation, data mining, and machine learning. Partners with business and IT teams to prepare datasets, identify process-improvement opportunities, and build predictive and computer vision models. Applies advanced statistical methods, develops testing scenarios, evaluates model performance, communicates insights to stakeholders, and supports model deployment and enhancement.

Leads the development, deployment, and evaluation of advanced machine learning models and scalable data science solutions. Implements MLOps practices, designs experiments, promotes data governance and ethical AI, and collaborates with data engineering, product, sales, IT, and business stakeholders to deliver actionable insights and strategic value. The role also identifies new data sources and tools, supports peer reviews, and advances AI-driven business solutions.

Leads rigorous evaluation methodologies for autonomous vehicle performance using statistical analysis, machine learning, simulation, and on-road data. Develops metrics, detects drift and anomalies, identifies edge cases, correlates simulation with fleet data, and strengthens launch-readiness and safety decisions. Partners with engineering, safety, and operations teams, communicates findings to leadership, establishes developer self-service tools, and mentors colleagues.

Develop and implement data science, predictive modeling, machine learning, simulation, and data engineering solutions for laboratory automation and digital R&D products. Lead pilots from opportunity identification through adoption, collaborate with scientists and technology teams, improve laboratory data quality and usability, integrate models into workflows, and measure business impact. The role requires technical leadership across multidisciplinary teams and contributes to the long-term Digital Lab roadmap.

Develops advanced analytics, mathematical models, algorithms, simulations, and data-processing tools for Army mission data. Builds and enhances software applications, supports MLOps infrastructure, evaluates machine learning deployment methods, and creates analytics solutions using large datasets. The role requires database expertise, programming proficiency, U.S. citizenship, and the ability to obtain a Secret clearance, with Interim Secret required before starting.

Lead marketing analytics and modeling to grow lending business: design experiments, build and validate campaign pipelines, measure funnel and ROI, manage direct-mail campaign selection and execution, create dashboards and provide data-driven recommendations to marketing and senior leadership.

Analyze business problems using SQL, Python or R, and statistical methods to identify trends, test hypotheses, and develop insights or recommendations. Visualize findings in Tableau and collaborate with data engineers to present results to stakeholders. The role requires clear communication, flexibility, and the ability to explain analytical approaches and adapt deliverables to business needs.

Develop predictive and statistical models, analytical datasets, data products, and AI-enabled workflows across marketing, ticketing, revenue, digital, and customer analytics. Analyze complex datasets, identify trends and data quality issues, validate AI-generated outputs, and translate business questions into scalable analytical solutions. Communicate technical findings and recommendations to technical and non-technical stakeholders.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms on structured and unstructured data; apply ML/AI/NLP techniques to deliver client-facing solutions. TS/SCI clearance required.

Supports data science solutions for natural gas utility operations through exploratory analysis, feature engineering, statistical and machine learning model development, forecasting, optimization, data validation, visualization, and documentation. The intern will work with operational, weather, telemetry, billing, and usage data; contribute to Microsoft Fabric pipelines; complete an assigned project; and present findings to data teams and leadership.

Translate business objectives into actionable data analysis, analyze large datasets, apply statistical and machine learning methods, and clearly communicate findings to technical and non-technical audiences. The role requires Hadoop ecosystem expertise, Python scripting, strong statistical knowledge, sampling and analytical skills, and business domain experience in retail, enterprise mobility, or healthcare.

Use data science to source, ingest, clean, transform, analyze, and visualize structured and unstructured data in WDP. Develop dashboards and data products (Databricks, SQL, Python, Tableau/Qlik), create visual narratives, and brief senior DoW leadership to support IT portfolio management.

Leads applied AI/ML research as Principal Investigator and technical leader. Defines research portfolios, experiments, evaluation plans, and transition criteria; remains hands-on with prototypes, models, code, and end-to-end AI architectures. Leads cross-functional teams, mentors technical staff, evaluates emerging technologies, and transitions successful research into secure, scalable production capabilities using software engineering and MLOps practices. Communicates technical strategies to customers, business leaders, and engineering teams.

Develop predictive sales analytics products using machine learning, statistical modeling, optimization, and advanced analytics. Aggregate, normalize, and process large, inconsistent datasets to generate actionable insights. Apply supervised and unsupervised learning, boosting, ensemble methods, and empirical research. Collaborate with teams to create innovative products and bring them to market, using tools such as Python, R, SQL, Hadoop, cloud platforms, and visualization software.

Analyze large datasets using data science, machine learning, predictive modeling, and big data technologies. Design approaches for data handling, search, retention, storage, and processing; develop software to improve data searches; validate data quality and processes; and report actionable findings. The role emphasizes scientific data work over visualization and requires an active Top Secret/SCI-eligible clearance with potential CI polygraph eligibility.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Designs, develops, evaluates, and deploys AI and data-driven solutions using machine learning, deep learning, NLP, LLMs, intelligent agents, prompt engineering, and RAG. Builds scalable data pipelines, experiments with models, deploys AI services through APIs and cloud infrastructure, and monitors system reliability and performance. Collaborates with engineering, product, and customer-facing teams to translate business needs into production-ready solutions while addressing risks and continuously improving AI capabilities.

Develop and deploy predictive and generative AI models for personalization, forecasting, marketing automation, conversational agents, and other business applications. Perform exploratory analysis, statistical modeling, causal inference, and A/B testing. Build Python-based modeling workflows, collaborate on productionization and MLOps, develop RAG and agentic systems, and communicate insights to technical and non-technical stakeholders across the organization.

Develops data analytics and machine learning solutions for vehicle diagnostics, prognostics, preventive maintenance, and intelligent controls. Processes high-volume engineering and service data using Hadoop, cloud systems, and modeling tools; identifies patterns, anomalies, and trends; supports validation, product improvement, research proposals, and analytics strategy. Collaborates with data engineering, software, engineering, suppliers, and business teams while guiding complex projects and technical roadmaps.