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.

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 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.

Leads end-to-end data science projects for automated underwriting and related insurance analytics. Develops statistical and machine-learning models, evaluates performance, analyzes complex datasets, and implements scalable production solutions. Partners with underwriting, product, business, engineering, and technology teams; monitors models; documents governance and validation processes; evaluates external data and third-party solutions; provides technical direction; reviews work; and mentors other data scientists.

The Data Scientist develops predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. The role analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.

Lead advanced data science for Unity’s advertising business, analyzing revenue, supply, demand, and customer behavior. Define metrics, evaluate ML and product initiatives, guide roadmaps, and turn large-scale data into executive-level recommendations. Partner with Product, Engineering, Sales, and Finance, while mentoring data scientists and providing technical leadership without formal management authority.

Data Scientist partnering with cross-functional teams to define metrics, design experiments, uncover insights, build analytical models, and develop data tools and systems. The role owns complex data projects end-to-end, supports product, marketing, finance, and platform initiatives, and promotes data quality and accessibility. Candidates need strong SQL, scripting, distributed data systems, statistical methods, experimentation, forecasting, and stakeholder communication skills.

Develop and maintain A/B experimentation and evaluation platforms for Glean’s AI products. The role designs rigorous statistical methods, evaluates LLM capabilities across tasks such as summarization, reasoning, tool use, coding, and system design, and establishes best practices for metrics, benchmarking, and experimentation. Responsibilities include productionizing scalable tools, analyzing product data, defining KPIs and guardrails, and partnering with engineering, product data science, product management, and design teams.

Leads AI and machine learning strategy, projects, and teams across the full model lifecycle. Responsibilities include developing and deploying models, establishing governance and MLOps standards, evaluating emerging technologies, managing AI risks, mentoring technical staff, partnering with stakeholders, and reporting business impact and delivery progress to senior leadership.