Top Data Science Jobs in Huntsville, AL

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Analyzes large datasets using data science, machine learning, predictive modeling, and big data technologies. Designs approaches for data handling, search, retention, storage, and processing; develops software to improve data searches; validates data quality and processes; and reports actionable insights. The role emphasizes scientific analysis over visualization and requires an active Top Secret/SCI clearance with potential Counterintelligence Polygraph eligibility.

Develop and deploy LLM and machine learning models for Airbnb Community Support. Responsibilities include automating LLM evaluation, curating synthetic datasets, analyzing customer issues, identifying model failure modes, building personalization models, exploring data, prototyping solutions, and communicating insights to cross-functional stakeholders. The role focuses on scalable, production-ready AI products and operational optimization.

Evaluate migration technologies, build quantitative models for migration risk and data quality, coordinate customer migration projects, develop data pipelines and analytical frameworks, implement Python and SQL validation workflows, assess cloud environments, and create dashboards and reporting for product, sales, and operations teams.

Develop clinically grounded data science and AI solutions using clinical, laboratory, commercial, and enterprise data. Responsibilities include machine learning and statistical modeling, generative AI, literature intelligence, exploratory analysis, feature engineering, validation, documentation, and deployment support. The role leads analytical workstreams and research collaborations, partners with clinical and business stakeholders, and communicates findings through reports, presentations, and decision-support tools while maintaining reproducibility, privacy, security, and audit-ready practices.

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.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Manage NASA aerospace portfolio data through cataloging, metadata standards, taxonomies, controlled vocabularies, data lineage, provenance, classification, and quality documentation. Evaluate and implement an enterprise data catalog, integrate it with Dagster, establish metadata and freshness metrics, onboard and retire datasets, and drive adoption across technical and program teams. Translate between data professionals and program stakeholders while improving data discoverability, trust, and usability.

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.

Perform epidemiologic analyses on surveillance, clinical, claims, lab, and survey data; develop analysis plans, statistical methods, and technical reports; build dashboards and automated analytic workflows; design data quality rules and surveillance indicators; translate findings for diverse stakeholders; support public health reporting, manuscripts, and dissemination; mentor junior staff and provide technical assistance.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Own the end-to-end analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define metrics, build reports, analyze data, design experiments, generate insights, and influence product and business decisions. Partner with stakeholders to clarify ambiguous problems, communicate findings, recommend actions, and drive measurable outcomes.

Own end-to-end data science for growth, product, AI, automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; translate findings into recommendations; influence stakeholders and drive measurable outcomes. The role requires strong SQL or Python skills, independent analytical ability, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Analyzes structured and unstructured datasets supporting U.S. Navy electronic warfare systems. Develops statistical, machine learning, NLP, data modeling, visualization, and reporting solutions to improve system readiness, reliability, maintainability, operational availability, and lifecycle performance. Integrates logistics, engineering, maintenance, and operational data; identifies trends and risks; and presents data-driven findings to technical and program stakeholders.

Develops analytical models and applications for U.S. Navy electronic warfare and sustainment systems. Applies data mining, statistical analysis, NLP, machine learning, and data modeling to structured and unstructured data. Integrates logistics, engineering, maintenance, reliability, and operational data to assess readiness, performance, risks, and availability. Creates visualizations, dashboards, reports, and briefings, while collaborating with engineers, logisticians, operations researchers, and program personnel to support lifecycle management and data-driven decisions.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.