Top Data Science Jobs in Austin, TX

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Owns experimentation, business analytics, reporting, and advanced analysis for Realtor.com’s RDC Next platform. The role analyzes consumer and agent behavior, designs A/B and quasi-experiments, builds trusted datasets and dashboards, and delivers recommendations that influence product and business strategy. It also develops AI-enabled analytics workflows using LLMs, RAG, and agentic tools; partners with Product, Engineering, Finance, Marketing, and data teams; mentors analysts; and helps establish analytical standards and scalable data solutions.

Leads product analytics, experimentation, and advanced analyses for consumer content and visualization experiences. Defines success metrics, designs tests, improves tracking and data quality, builds ETL pipelines and AI-enabled self-service reporting, and translates consumer behavior insights into product and business strategy. Partners with Product, Engineering, Finance, and data teams to identify opportunities, measure performance, and influence the product roadmap.

Leads CRM marketing analytics for Seller and Owner verticals by developing segmentation, targeting, personalization, customer journey, and campaign measurement strategies. Analyzes consumer behavior to improve engagement, retention, lead generation, cross-selling, and revenue. Builds statistical and machine learning models, integrates CRM data, establishes KPIs, communicates insights to stakeholders, and partners with Marketing, Data Engineering, and IT teams.

Leads advanced data science modeling and provides independent second-line oversight of Buy Now Pay Later credit risk, underwriting, account management, risk controls, and exposure limits. The role analyzes portfolio performance, ensures data quality, challenges credit strategies, communicates insights to technical and non-technical stakeholders, and mentors junior data scientists while collaborating across geographically distributed teams.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

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.

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.

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.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Own and improve sales quota-setting models and methodologies using statistical modeling, forecasting, optimization, machine learning, and AI. Validate data, assumptions, model outputs, and business impact while partnering with Finance, Sales Compensation, Analytics, Engineering, and business stakeholders. Develop scalable analytical workflows and LLM solutions, including RAG applications, and communicate quantitative findings clearly to influence sales strategy, incentive programs, and operational decisions.

Develop and implement agentic and generative AI solutions, including LLMs, RAG, AI security, and related platforms. Support research and development, technical proposals, prototyping, and client consulting. Apply data science and engineering best practices, integrate and analyze data, gather requirements, and translate technical concepts for clients and stakeholders.

Supports migration of reporting assets from SAP BusinessObjects to Qlik by rebuilding, testing, validating, documenting, and troubleshooting reports. Responsibilities include creating dashboards, writing SQL queries, updating Qlik scripts and expressions, tracking migration changes, migrating scheduled jobs, and supporting user acceptance testing and training documentation under senior developer guidance.

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.

Lead discovery and development of intelligent payment products using data science and machine learning. Analyze large-scale transaction data to optimize routing, interchange, acceptance, fraud risk, supplier connectivity, customer acquisition, and payment economics. Build predictive models, recommendations, optimization signals, and decisioning capabilities; validate business value through experimentation; and partner with Product and Engineering to productionize solutions. Provide technical leadership, mentor analysts, and advise senior Payments stakeholders on high-value opportunities.

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.

Leads product analytics for Xbox Growth, using statistical, econometric, causal inference, experimentation, forecasting, and machine learning methods to guide product strategy, measure engagement and monetization impact, and identify growth opportunities. Partners with Product, Engineering, and executives, builds reusable analytical tools and self-service capabilities using agentic AI, influences investment decisions, and mentors data science team members.

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.

Develop autonomous AI agents capable of goal setting, decomposition, planning, tool use, memory, and long-horizon execution. Build evaluation frameworks for alignment, reliability, and safety; identify goal misalignment and undesirable behaviors; and integrate agent systems with APIs, databases, search engines, and other tools. Responsibilities include designing production-ready machine learning systems, collaborating across teams, and advancing agent autonomy through planning, causal reasoning, model-based reinforcement learning, and language modeling.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.