Top Data Science Jobs

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.

Leads predictive modeling and machine learning initiatives to solve business problems and improve member experiences. Provides methodology guidance, coaches junior data scientists, develops and optimizes models, processes massive structured and unstructured datasets, manages batch and real-time scoring, and communicates findings to business partners. The role requires advanced statistical modeling, distributed computing, cloud analytics, deep learning, data engineering, and healthcare data analysis involving medical records, claims, and eligibility data.

Develops scalable self-service analytics, data science frameworks, predictive and causal models, experimentation strategies, semantic layers, and AI-powered analytics tools. Partners with Product, Engineering, Design, and data engineering teams to improve product adoption, engagement, retention, and monetization insights. Leads complex analytics initiatives, ensures analytical rigor and governance, communicates findings to executives, and mentors analysts and data scientists.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Build LLM-powered research and analysis products for political strategists. Partner with users to shape workflows, define quality standards, design evaluations and LLM-as-judge systems, prototype features, and communicate technical tradeoffs to technical and nontechnical stakeholders. The role requires strong Python, SQL, statistical and machine learning skills, hands-on LLM and embedding experience, product judgment, and comfort with ambiguous problems.

As a Data Scientist at Opendoor, you'll enhance pricing strategies through statistical models, machine learning, experiments, and interdisciplinary collaboration to drive business outcomes.

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 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.

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.

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.

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.

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.

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.

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.

Develop causal inference, experimentation, predictive, uplift, and treatment-effect models to measure business impact and improve targeting. Design and analyze A/B tests, work with large datasets, build scalable analytical frameworks and pipelines, and communicate actionable recommendations to technical and business stakeholders.

Develop and deploy data science and AI/ML solutions for scientific, engineering, and business problems within R&D. Responsibilities include data preparation, statistical modeling, machine learning, deep learning, computer vision, generative AI, LLM evaluation, predictive modeling, visualization, and pipeline development. The role collaborates with data engineers, scientists, domain experts, and stakeholders to scale AI solutions and communicate technical findings.

Build and validate analytical models using large structured datasets to improve operational and business outcomes. Translate complex findings into actionable recommendations, partner with engineering, design, and product teams to deploy production-ready solutions, lead high-impact federal projects, mentor junior staff, and shape data science strategy for Department of Education programs.