Top Hybrid Data Science Jobs

Analyze behavioral data, evaluate product experiments, model user behavior, develop dashboards and forecasts, ensure tracking quality, and provide insights that influence product decisions. Collaborate with product and engineering teams while applying statistical, analytical, and AI methods to improve user experiences and business outcomes.

The Senior Data Scientist will develop, validate, and operationalize AI models for diagnosing complex diseases, enhance data analysis processes, and establish MLOps infrastructure while leading data-driven initiatives.

Own product experimentation and growth analytics across signup, activation, conversion, monetization, and retention. Design and analyze A/B tests, establish statistical rigor, conduct funnel, cohort, LTV, segmentation, and retention analyses, and define trusted product metrics. Partner with Product, Engineering, Growth, and executives to translate insights into product decisions and recommendations.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Founding Data Scientist responsible for developing product analytics, earned media value metrics, attribution models, forecasting algorithms, and proprietary social commerce measurements. The role partners with engineering and product teams to improve data foundations, translate models into production code, and support public-facing data reports. Requires advanced Python, expert SQL, production software engineering skills, quantitative education, strong communication, and 4–7 years of data science experience.

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.

Develops and optimizes NLP and machine learning pipelines for medical and claims data supporting VA disability claims adjudication. Responsibilities include extracting and analyzing healthcare data, tuning transformer-based models, validating performance, documenting workflows, collaborating with VA stakeholders, and supporting model integration into government systems. The role requires Python, NLP, SQL, healthcare data knowledge, and experience delivering data science projects in regulated environments.

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.

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.