Top Data Science Jobs

Leads data science research from question framing through modeling, validation, and recommendations. Partners with executives, business leaders, ML engineers, and data engineers to identify opportunities in risk, fraud, retention, pricing, and compliance. Builds forecasts and production-ready models, evaluates data as a strategic asset, and translates insights into product features, offerings, and measurable business outcomes.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Analyze contact center, customer, operational, and Voice of Customer data to identify trends, performance gaps, and process improvements. Build dashboards, analytics workflows, AI solutions, automation tools, and prototypes using Python and related technologies. Partner with business and technical stakeholders, analyze call and CRM data, develop coaching recommendations, and present actionable insights and business cases that improve agent productivity, call quality, customer experience, and contact center operations.

The Data Scientist will support national security projects by developing and maintaining bulk data pipelines, processing structured and unstructured data, performing ETL and data quality analysis, and creating reports and dashboards. Responsibilities include using Spark, PySpark, Python, SQL, cloud services, and log-processing tools; documenting ETL designs; collaborating with customers and integration partners; and supporting Agile development. Preferred experience includes Databricks, Elastic MapReduce, Spark optimization, AWS services, and the ELK stack.

Build analytical datasets and pipelines using SQL, Python, and Snowflake; develop descriptive, predictive, and prescriptive analyses; create BI dashboards; conduct exploratory analysis; and translate cross-functional business questions into data-driven recommendations for Finance, Operations, Marketing, and Development.

Unpaid remote internship focused on data extraction, scraping, mining, processing, cleaning, pipeline development, AI model training, and containerized microservices. The intern will use Django, Puppeteer, Selenium, Docker, Pandas, NumPy, Scikit-learn, Git, and GitHub while applying testing and data engineering practices. The role requires at least 30 hours weekly and may convert to full-time employment.

Develop and deploy machine learning, statistical modeling, and advanced analytics solutions supporting audit, risk management, cybersecurity, fraud detection, and operational efficiency. Manage data science projects from development through implementation, deliver actionable insights, mentor junior data scientists, and contribute to team strategy. The role is initially in-office in Charlotte, North Carolina, transitioning to a hybrid schedule after 90 days.

Develop and deploy machine learning, statistical modeling, and advanced analytics solutions for audit, risk management, cybersecurity, and fraud detection. Manage data science projects from development through implementation, deliver actionable insights, mentor junior data scientists, and help shape the team’s strategic direction while addressing complex data challenges.

Build and deploy production machine learning systems for AIOps and IT operations. Develop novel AI/ML approaches, improve model reliability and accuracy, and work with large-scale operational and text data. Apply statistical modeling, anomaly detection, forecasting, clustering, NLP, causal inference, and root cause analysis. Collaborate with product and engineering teams, guide model and system design decisions, communicate findings, and mentor engineers while supporting production ML services.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead data science and AI/ML initiatives, identifying opportunities, developing and deploying models across millions of devices, and conducting experimentation. Collaborate with product, engineering, and cross-functional teams to deliver roadmap outcomes, communicate insights, manage risks, and influence strategy. The role requires advanced quantitative education, 10+ years of industry experience, and expertise in SQL, Python, PySpark, machine learning, data transformation, and statistical modeling.

Leads the design, development, deployment, and lifecycle management of production-grade data science, machine learning, AI, PHM, and LLM solutions for oil and gas equipment. Builds predictive maintenance, reliability, optimization, dashboards, and RAG applications; manages ModelOps, cloud data platforms, edge deployments, governance, and model monitoring; and mentors data scientists while guiding analytics architecture.

Develop and deploy Python-based data science and machine learning solutions for national security missions. Process petabyte-scale data on supercomputing resources, build automation, work with SQL and NoSQL databases, and support MLOps workflows. Collaborate with data engineers and government end users to improve data quality and access, using Linux, Docker, GitOps, CI/CD, and Agile practices. Document processes and maintain clean, testable code.

Own the end-to-end development of recommendation and personalization models for a large commerce catalog. Design embedding, retrieval, ranking, and cold-start strategies; conduct offline evaluations and online experiments; create features from behavioral and catalog data; apply NLP, computer vision, and LLM techniques; and deploy, monitor, and improve production models on the existing MLOps platform.

Lead data science and analytics initiatives that optimize Coalition’s cyber insurance business. Responsibilities include experimentation, statistical analysis, funnel metric development, executive dashboards, data modeling, scalable analytical workflows, and data-backed product and revenue recommendations. The role partners with product, engineering, and insurance stakeholders, supports analytics agents, mentors junior team members, and establishes standards for code quality and documentation.

Lead data science strategy across product, engineering, and executive teams. Design experimentation and causal inference frameworks; architect statistical and machine learning models for bidding, segmentation, recommendations, attribution, forecasting, and lifetime value. Establish scalable metrics, deployment, monitoring, and modeling practices with infrastructure and data engineering teams. Mentor data scientists, set technical standards, influence product strategy, and translate complex findings into executive recommendations.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Leads the design and delivery of production AI and data science systems combining classical machine learning, retrieval, ranking, and large language models. Owns projects from problem framing through deployment, evaluation, and iteration; contributes to scalable architecture and product decisions; and mentors data scientists. Requires strong Python, machine learning, LLM, search, vector database, and software development expertise, along with cross-functional collaboration and technical leadership.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Leads enterprise credit data science and AI initiatives, including NLP and LLM proof-of-concepts, model development, validation, deployment, monitoring, and governance. Partners with business and technical stakeholders to identify insights, manage risk, document controls, and evaluate scalable solutions. Mentors junior data scientists, communicates recommendations, and supports responsible adoption of AI across regulated banking environments.

Develop predictive, descriptive, and prescriptive models using member, financial, demographic, geographic, and behavioral data. Analyze market opportunities, membership growth, product adoption, and engagement to support branch strategy. Apply statistics, machine learning, forecasting, and trend analysis; build automated analytical processes; communicate insights to stakeholders; and collaborate with business and geospatial partners. Lead small projects and support ethical AI and data-driven decision-making.

Leads advanced analytics, artificial intelligence, predictive modeling, simulations, forecasting, and optimization initiatives supporting Navy readiness modernization. Develops solutions for supply chain, sustainment, inventory, and operational readiness; establishes model governance and validation practices; translates operational needs into recommendations; mentors data professionals; collaborates with government stakeholders; and presents findings to senior leaders.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Leads data science initiatives for Capital One’s Card Mainstreet business. Partners with data scientists, software engineers, and product managers to develop customer-focused solutions; analyzes large-scale numeric and textual data; builds, validates, and implements machine learning models; translates technical findings into business outcomes; and supports innovation and team talent development.