Top Senior Data Science Jobs in Austin, TX

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive decisions and outcomes with cross-functional stakeholders. The role requires strong SQL or Python skills, independent data work, analytical problem-solving, and leadership-level influence.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent data work, strong analytical thinking, clear communication, and ownership of ambiguous problems that deliver measurable business impact.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Designs AI risk stratification and evaluation methodologies for Veterans Health Administration systems, assessing performance, fairness, value, and post-deployment monitoring. Provides technical guidance on machine learning, healthcare data models, cohorts, synthetic data, and pilot evaluations. Develops risk management materials and supports governance decisions through quantitative analysis. Requires extensive applied data science experience, advanced education, and expertise in healthcare-focused machine learning.

Lead statistical modeling, AI/ML evaluation, data governance, data quality assurance, and executive analytics for VA EHR modernization. Build reproducible R-based analytics, reporting pipelines, dashboards, and governed data products using R, SQL, Python, Git, and cloud platforms. Evaluate model reliability, bias, drift, and operational value; integrate complex healthcare data; define metrics and validation rules; and advise stakeholders on auditable analytical practices.

Build and deploy predictive models for workforce attrition, turnover, hiring, and headcount forecasting. Develop AI agents that query and act on sensitive People data, using LLMs, tool orchestration, retrieval, and guardrails. Apply experimentation, causal inference, and uncertainty quantification; extract and structure complex HR data with SQL and APIs; monitor model performance; partner with HR and Data Engineering; and explain findings to nontechnical leaders.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

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.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Develop and deploy statistical and machine learning models supporting credit risk, fraud, collections, and marketing. Responsibilities include data exploration, model design, testing, production monitoring, performance analysis, governance, and stakeholder communication. The role partners with Legal and Compliance, translates business questions into analytical solutions, researches new methods, and provides data-driven recommendations to leadership.

Design and deploy predictive forecasting and analytics to support Revenue operations. Build models for forecasting, churn, expansion, and pipeline analytics; create scalable dashboards; partner with Revenue Operations, Finance, and Data Engineering to improve data quality, KPI governance, and executive reporting to drive data-driven decisions and AI readiness.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Leads ambiguous, large-scale data science initiatives across multiple teams; defines organizational methodologies and best practices; translates research into scalable solutions; designs MLOps pipelines for model deployment and monitoring; mentors senior data scientists; and advises executive leadership on strategic and technical decisions.

Leads the design, development, and deployment of enterprise AI, Generative AI, Agentic AI, RAG, machine learning, and advanced analytics solutions. Builds AI assistants, intelligent agents, conversational data interfaces, semantic search tools, APIs, and scalable analytical platforms. Develops data models and datasets across Snowflake, GCP, and BigQuery while supporting data architecture, governance, and quality. Partners with business and technical teams to identify opportunities, translate requirements, advise stakeholders, and demonstrate AI and automation value.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Build and deploy machine learning and experimentation systems for personalization, ranking, targeting, attribution, and causal measurement. Develop features and pipelines from transaction and behavioral data, design A/B tests, productionize models with engineering teams, and improve recommendation systems. Translate analytical results into business insights tied to conversion, retention, lift, and ROI while owning projects from problem framing through post-launch evaluation.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.