Top Hybrid Data Science Jobs

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.

Use statistical, algorithmic, and visualization techniques to model business problems, derive actionable insights from member data, and inform product and service decisions. Collaborate with end-users, build analytic capabilities, communicate data-driven recommendations, and promote advanced analytics adoption across the organization.

Lead client engagements using data science, AI, advanced analytics, statistical modeling, experimentation, and marketing measurement. Manage and develop analysts and data scientists, advise quantitative researchers, pitch projects, grow client relationships, and provide thought leadership on machine learning and emerging AI methods. Translate technical work into strategic recommendations for clients and executives.

Analyze marketing and business performance using SQL, statistics, econometric modeling, Bayesian regression, causal inference, and A/B testing. Build marketing measurement models, integrate customer and event data, develop reporting infrastructure and dashboards, evaluate marketing spend, and provide actionable recommendations to optimize growth. Partner with Marketing, Product, Engineering, Finance, and executives across multiple brands.

Develops and deploys interpretable, performant analytics and predictive modeling solutions that support business strategy, process improvement, fraud detection, portfolio optimization, and growth. Collaborates with business experts, data scientists, engineers, and IT teams to connect complex data sources, build scalable analytics tools, generate actionable insights, and deploy analytical content into business applications. Also helps business partners improve their data and analytics capabilities.