Top Data Science Jobs in Nashville, TN

Embed with a core business unit to define problems, design experiments, build reports, run analyses, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Design, validate, and operationalize predictive and machine learning models; mine and analyze large datasets; create visualizations and statistical validation; collaborate with teams to integrate solutions and communicate results to stakeholders.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Apply machine learning, computer vision, and advanced analytics to business and scientific problems. Build and visualize data-driven solutions, design data science applications, work with cloud and distributed data ecosystems, collaborate across global teams and partners, and deliver deployable ML products in agile teams.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Design and develop predictive and prescriptive analytics, machine learning models, and BI solutions. Build ETL pipelines, extract and validate data, create dashboards and visualizations (Power BI), and provide data-driven insights for aftersales service retention. Collaborate with stakeholders to gather requirements, troubleshoot issues, document processes, and support ongoing iterative development.

The Principal Data Scientist will lead data analysis projects, collaborate with product management, assess business impacts, ensure data governance, and mentor junior analysts. Must leverage statistical techniques and machine learning to drive product strategy and business outcomes.

Lead a data science team developing data-driven solutions for CMS Medicare quality reporting. Build and optimize predictive models and backend jobs using Python, PySpark, and Databricks. Conduct code reviews, debugging, documentation, ML monitoring, and stakeholder collaboration. Ensure deliverable quality, participate in Agile ceremonies, and drive process and performance improvements.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Design, develop, and deploy NLP and LLM-powered models to extract intent, outcomes, and sentiment from calls, chats, and digital interactions. Translate unstructured interactions into actionable insights, build evaluation frameworks, debug and improve model performance, and hand off production-ready artifacts to data engineering to improve call center efficiency and customer experience.

Lead data-driven strategy by partnering with senior executives to deliver rigorous analytical solutions. Design and implement sophisticated analyses on large-scale data, build trusted cross-functional relationships, mentor data scientists, and influence long-term product and business decisions.

Lead data-driven strategy by partnering with senior executives, design and deliver sophisticated analytical solutions on large-scale data, build trusted cross-functional relationships, and mentor data scientists to raise analytical quality across the organization.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

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

The role involves developing graph algorithms, data pipelines for identity verification, and supporting data analysis for compliance products.

The Staff Data Scientist will design and optimize deep learning models for fraud detection, mentor peers, and lead data science initiatives.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

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 strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

Build, evaluate, and ship matching, recommendation, and ranking models for job-seeker experiences. Work with skills and occupation taxonomies and labor-market data, partner with Engineering to productionize and monitor models, and translate messy data into clear, actionable analysis for product and customers.