Top Data Science Jobs in Austin, TX

As a Data Scientist, you'll develop and validate machine learning models, implement AI solutions, and conduct fieldwork in support of customers.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, surface proactive insights, optimize marketing spend, and collaborate with Data/Analytics Engineers to build data models and pipelines. Communicate findings and recommendations to non-technical stakeholders.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead product-focused analytics and experimentation to improve core experience and AI initiatives. Build propensity models and segmentation, create automated dashboards and executive reports, run and monitor experiments, and partner cross-functionally to translate analysis into business impact and drive product decisions.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

The role involves analyzing financial fraud data, developing methods for investigation, and presenting findings. Expected to manage projects over time.

Serve as a senior analytics partner for consumer product experiences, owning experimentation, business analytics, and data to inform product roadmaps. Lead exploratory analyses, design multivariate tests, define success metrics, improve tracking and data quality, and build self-service AI-enabled reporting to surface insights about consumer behavior, content engagement, and media quality that drive product and business decisions.

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

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.

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

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

Embedded on a core business team, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions across growth, product, AI, or partnerships.

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.

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

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.

Design, build, and deploy predictive GTM models (LTV, attribution, propensity). Lead experimentation and causal analyses, define KPI measurement frameworks, partner with Data Engineering, and translate statistical insights into actionable recommendations for Sales, Marketing, and Customer Success.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.

Lead CRM marketing analytics for Seller and Owner verticals: build segmentation and personalization models, map customer journeys, design targeted campaigns, define KPIs, analyze consumer data, integrate CRM with other systems, collaborate with engineering and marketing to optimize engagement and drive revenue.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

Lead and execute statistical and geospatial analyses to quantify and map climate mitigation potential from forests and peatlands. Design analytical workflows, manage projects and partners, create datasets, tools, reports, and deliver trainings and technical support for global conservation initiatives. Communicate results via publications, visualizations, and capacity-building activities; occasional fieldwork and international travel possible.