Top Data Science Jobs in Columbus, OH

Develops data-driven insights for Business Banking deposit and cash management products. Partners with product managers to translate business questions into analyses, forecasting, diagnostic deep dives, and performance reporting. Communicates findings to nontechnical stakeholders, supports product strategy and customer engagement, and maintains strong data quality and controls. The role requires advanced SQL, experience across on-premises and cloud databases, and use of large language models to improve code development.

Analyze campaign performance, funnel conversion, lead quality, and outreach effectiveness for home lending. Design A/B tests, measure lift, interpret predictive model outputs, merge data from multiple sources, and identify actionable trends. Use GenAI/LLM tools responsibly to accelerate exploration while validating conclusions. Partner with marketing, sales strategy, and cross-functional teams to optimize campaigns, pilot initiatives, and transition successful programs into production. Create transparent stakeholder deliverables including analyses, presentations, and dashboards.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Leads data science for the American Dream Initiative by developing customer outcome metrics, financial health indicators, personas, automated reporting, OKR frameworks, and experimentation programs. Integrates large-scale data, performs analyses and modeling using SQL and Python, and delivers executive-ready insights for targeting, personalization, and customer engagement. Partners cross-functionally, mentors team members, and ensures responsible analytics, compliance, and model-risk practices.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

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.

Own recurring reporting and ad hoc analysis of consumer deposit account openings across checking, savings, and CDs. Develop automated performance views, identify business drivers, ensure data quality and reproducibility, and present concise insights and recommendations to senior executives. Partner with business, finance, product, marketing, and data teams to validate results and connect trends to business activity.

Develops and deploys GenAI and machine learning solutions for complex legal business problems, including semantic search, question answering, document analysis, and service automation. Responsibilities include data analysis, model training and evaluation, generative model optimization, LLM solution development, RAG and agentic AI implementation, and communicating technical insights to business stakeholders. The role collaborates with product managers, data scientists, ML engineers, and engineering teams to operationalize production AI applications.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Build and scale AI-driven analytics and decision-support products for Finance. Responsibilities include prototyping LLM agents, multi-agent workflows, RAG, forecasting, evaluation harnesses, guardrails, data architectures, semantic layers, observability, and data governance. The role also owns AI and data roadmaps, experimentation frameworks, and business-impact measurement while partnering with Finance, engineering, and platform teams.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

Data Scientist Interns will collect, clean, analyze, and visualize large datasets; develop statistical and machine learning models; collaborate with cross-functional teams; present insights to stakeholders; and document methodologies. The role requires study in a quantitative or computer science field, programming proficiency, statistical knowledge, and familiarity with data science libraries and frameworks.

Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Develop and refine credit risk strategies, rules, monitoring tools, and performance dashboards. Use SQL, Python, statistics, and data science to analyze complex datasets, identify risk trends, optimize processes, and influence business decisions. Collaborate with product, engineering, and operations teams to implement strategies across the customer lifecycle, while mentoring junior team members.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative reasoning problems. Create expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling expertise.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.