Top Data Science Jobs in Memphis, TN

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

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.

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.

Develops fraud detection models using client data, including data cleaning, feature engineering, model tuning, and scalable pipeline development. Collaborates with clients and internal product and research teams, communicates technical findings to diverse audiences, guides project execution, and contributes to generalized product enhancements and fraud prevention solutions.

Develop and evaluate machine learning models for fraud detection across account opening, account takeover, and identity risk. Responsibilities include exploring large datasets, identifying fraud patterns, engineering features, testing models, writing production-quality Python code, supporting model monitoring, and communicating analytical findings. The role also applies data privacy, explainability, validation, documentation, and governance standards while collaborating with senior data scientists and engineering teams.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Leads healthcare quality analytics and revenue optimization by analyzing clinical and administrative data, translating CMS and payer methodologies into analytical frameworks, and developing predictive models for quality ratings and value-based payment programs. Creates dashboards, early-warning systems, and statistical analyses; presents data-driven recommendations to leadership; collaborates with quality, coding, CDI, finance, IT, and data governance teams; and mentors analysts while improving analytics processes and data accessibility.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.

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.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Analyze digital customer behavior using SQL, Python, and PySpark to generate actionable business insights. Build automated analytics workflows, Power BI dashboards, data visualizations, and customer-level models. Partner with Digital, IT, Decision Science, and external stakeholders to interpret large datasets, communicate findings to nontechnical audiences, and support digital engagement, coupon, cashback, and loyalty initiatives.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

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.

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.