Top Senior Data Science Jobs in Columbus, OH

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Build and own measurement systems, MMM, incrementality, and experimentation frameworks to quantify marketing incrementality, optimize budget allocation, forecast LTV/CAC, and operationalize production-grade models and metrics to drive growth, retention, and long-term enterprise value.

Lead design and ownership of advanced fraud detection models and monitoring. Apply adversarial threat modeling to select architectures (tree-based, deep, graph), debug production failures, run experiments, mentor engineers, and build AI-assisted workflows that scale domain expertise while resisting manipulation.

As a Data Scientist, you will develop advanced fraud detection models, collaborate with teams, analyze data for insights, optimize models, and ensure compliance with data privacy.

As a Business Intelligence Data Scientist, you will develop dashboards, build statistical models, and analyze product features to enhance user insights.

The Staff Data Scientist will oversee Foodsmart's member growth analytics, focusing on marketing and product funnel optimization, experimentation, attribution modeling, and enhancing self-service analytic tools while collaborating closely with leadership and cross-functional teams.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

This role involves partnering with sales to design data-driven solutions, engaging with customers, and supporting pre-sales engagements.

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

The role involves designing and deploying ML models for yield optimization, pricing, inventory allocation, and developing real-time decision systems within a programmatic advertising context, collaborating across teams and ensuring production-ready implementations.

The Senior Data Scientist will develop and deploy machine learning models for yield optimization, pricing, and inventory allocation in programmatic advertising. Responsibilities include building data pipelines, real-time inference systems, conducting experiments, and collaborating with product and engineering teams to enhance marketplace performance.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

Design, build, and deploy search and recommendation ML systems (ranking, retrieval, personalization, query understanding) using embeddings and LLM approaches. Collaborate with engineers and product to create scalable data pipelines, integrate models via APIs, run A/B tests, and monitor online/offline metrics to improve relevance and engagement.

Lead data science across Growth and Membership Operations: define problems, build scalable data models and pipelines, develop analytical and ML solutions, partner cross-functionally, and integrate AI tools to optimize sales, marketing, telesales, and enrollment operations.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

Lead advanced analytics and ML for partner-facing teams: build end-to-end predictive models (churn, lead scoring, health scores), marketing attribution and mix models, and maintain ML lifecycle standards. Develop agentic workflows and Claude Skills to accelerate work, mentor analytics team, and translate model outputs into actionable insights for Sales, Marketing, and CX.

Develop and validate SDTM and ADaM datasets using R per CDISC standards, support TLF generation in R or SAS, perform QC and reconciliation, collaborate with biostatistics and clinical teams, contribute to automation, documentation, and version control for regulatory-compliant clinical trial deliverables.

Design, implement, and evaluate advanced NLP/NLU systems for clinical text using transformer and research-oriented neural architectures. Prepare and enrich clinical datasets, run experiments and error analyses, mitigate model risks (bias, drift, hallucination), and partner with ML engineers to deploy models into a compliant cloud environment.

The Senior Data Scientist will design and deploy machine learning models, analyze complex datasets, develop analytical solutions, and mentor junior team members in a remote environment.

Lead retention and churn efforts by building survival and lifecycle-state models, classifying churn reasons, creating personalized win-back and next-best-action interventions, optimizing offers/promos, designing uplift experiments, and owning production model lifecycle, monitoring, and MLOps across Product, CRM, Marketing, and Engineering.