Top Senior Data Science Jobs in Columbus, OH

Analyze Core customer behavior, engagement, retention, product usage, and economics to identify strategic opportunities. Translate complex analyses into executive-ready insights, presentations, and recommendations. Partner with product, marketing, finance, technology, data science, and governance teams to shape customer growth priorities. Apply AI, statistics, machine learning, SQL, and visualization techniques to solve unstructured business problems while aligning recommendations with business, risk, and compliance standards.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

Leads product analytics for small business lending by framing business questions, building and validating datasets, developing KPI and reporting frameworks, conducting statistical and predictive analyses, and translating findings into executive-ready recommendations. Partners with product, lending strategy, risk, finance, technology, and operations stakeholders to improve measurement, automation, data governance, customer understanding, and lending-product performance.

Lead analytics for new account performance and deposit growth, run and interpret A/B and incrementality tests, perform ad-hoc customer behavior analyses, present insights to senior stakeholders (including the CMO), and advance analytics methods while maintaining reproducible, controlled reporting.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Leads marketing analytics strategy for Chase Consumer Bank referral and prospecting campaigns. Measures campaign effectiveness, designs experiments, analyzes customer referral behavior, and translates results into recommendations for senior stakeholders. Mentors junior data scientists, drives advanced analytics innovation, recruits and trains data professionals, collaborates across analytics teams, and maintains rigorous controls for accurate and timely results.

Deliver data-driven insights for Business Banking strategy using statistical analysis, predictive modeling, forecasting, text mining, Python, SQL, Snowflake, Databricks, and Tableau. Collaborate with senior stakeholders, manage large and diverse datasets, ensure data quality and security, support projects from requirements through delivery, and present actionable findings to senior leaders and investors.

Apply statistical analysis, predictive modeling, experimentation, and AI-assisted methods to support product analytics for small business P&C insurance. Partner cross-functionally to build lead/trigger capabilities, measurement frameworks, and AI-enabled use cases to improve client outcomes and product performance.

Lead credit-risk analytics for consumers and small businesses using large payment/customer data sets. Build attributes and models, deliver executive-ready insights, monitor emerging risks, prioritize high-impact projects, and identify GenAI opportunities to modernize analytics.

Lead advanced analytics and ML efforts to extract insights from large-scale historical data, design features and data representations for models and LLM/agentic systems, partner with product and engineering to build data pipelines and prototypes, evaluate AI workflows, and drive innovation through experiments and empirical analysis to inform product strategy.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Design, develop, and productionize ML/AI and statistical models to forecast demand, optimize capacity, and enable real-time workforce decisioning. Collaborate with stakeholders, select principled modeling approaches, ensure model robustness, and communicate actionable insights to operations and leadership.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead people analytics initiatives by applying advanced data science and machine learning to HR data. Advise HR leaders, build analytics strategy and models, ensure data quality and governance, develop dashboards and automated solutions, translate findings into decision-ready insights, and support cross-functional data pipelines and measurement best practices.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.