Top Hybrid Data Science Jobs

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.

Data Science Interns will collaborate with product and engineering teams to translate business questions into data problems, evaluate experiments, develop behavioral and growth models, and build scalable data pipelines and tools. Projects may focus on analytics, experimentation, infrastructure, user behavior, KPI research, and funnel analysis. Strong coding, statistics or data systems knowledge, communication, and problem-solving skills are expected.

Leads advanced data science and machine learning initiatives addressing complex business challenges. Develops predictive models, analytical strategies, and data visualizations to drive business decisions. Partners with cross-functional teams to create scalable data solutions, maintain data quality and governance, and translate requirements into measurable outcomes. Mentors data science professionals and advances analytics best practices across Walmart’s Marketplace Engineering organization.

Analyze structured and unstructured data, develop and test statistical/analytical models, prepare and validate data, partner with engineers and product teams to build end-to-end analytical solutions, evaluate new data sources and methods, document approaches, and communicate findings to stakeholders.

Build and deploy machine learning, predictive marketing, forecasting, and experimentation models. Develop scalable data pipelines, integrate diverse data sources, maintain data integrity, and deliver executive reporting insights. Analyze customer and market trends, support marketing measurement and product positioning, and translate complex technical findings into strategic recommendations. The role also involves mentoring junior staff and collaborating with IT to deploy analytical solutions.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.

Develop data science, machine learning, AI, statistical, and visualization solutions supporting government audit and investigative products. Build and optimize data pipelines, integrate diverse data sources, perform ETL and modeling, troubleshoot applications, and document technical designs. Collaborate with stakeholders, database administrators, and IT teams; provide technical guidance, training, and support. The role requires coding in SQL, Python, R, and JavaScript, cloud data platform experience, U.S. citizenship, and eligibility for Public Trust clearance.

Designs, deploys, and optimizes machine learning solutions for large-scale data platforms, including identity resolution, anomaly detection, data quality, predictive modeling, and Generative AI applications. Leads data science research and platform improvements, manages ingestion and transformation into BigQuery, analyzes pipeline performance, and performs root-cause investigations. Partners with engineers, architects, privacy, product, and global stakeholders while mentoring team members and translating business needs into technical specifications.

Data Science Interns will develop optimization, machine learning, or inference models supporting Lyft’s marketplace and transportation platform. Responsibilities include exploratory data analysis, production modeling, algorithm implementation, simulated and live experiments, statistical analysis, and communicating findings to guide launch decisions. Interns collaborate with engineers, product managers, and cross-functional partners. Candidates must be pursuing a relevant master’s or PhD and be available in Summer 2027 for a hybrid internship in New York City.

Develop and implement statistical, machine learning, predictive, and optimization models to solve business challenges. Design data-driven experiments, prepare and validate structured and unstructured data, create visualizations, and communicate findings to technical and non-technical stakeholders. The role also leads adoption of new data science techniques and supports continuous improvement of organizational practices.