Top Data Science Jobs in Houston, TX

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead development of interactive safety visualizations, dashboards, analytical applications, data marts, and automated reporting for autonomous-driving safety. Build trusted, performant data products with quality monitoring, frequent refreshes, metric definitions, lineage, and statistical interpretation. Partner with safety engineers, data engineers, program teams, and executives to translate complex safety information into clear decision-support tools and continuous improvement insights.

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.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

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 design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Design, research, and deploy NLP/GenAI surveillance models (LLMs, RAG, knowledge graphs) to detect suspicious financial behavior; build production-ready systems, collaborate with Compliance/Legal/IT, and drive adoption of AI solutions.

Develop AI-enabled applications, intelligent workflows, agent-based solutions, and enterprise automation. Build scalable data pipelines, APIs, integrations, analytics-ready datasets, and structured data models. Apply LLMs, embeddings, vector databases, semantic search, and orchestration frameworks to prototype and deploy practical AI solutions. Collaborate with technical and business stakeholders to identify transformation opportunities, support experimentation, and ensure scalable adoption across commercial, operational, product, marketing, and technology functions.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Develop and manage production-grade machine learning models for wholesale power and gas markets, including price forecasting, congestion analysis, load forecasting, and trading algorithms. Own the ML lifecycle from feature engineering and model selection through deployment, monitoring, and refinement. Analyze ERCOT and PJM market drivers, build data visualizations, apply LLMs, collaborate with technical and business stakeholders, and communicate insights that support trading, risk mitigation, and operational decisions.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Lead design and delivery of large-scale AI/ML and retrieval systems for cybersecurity. Build end-to-end ML architecture, models (anomaly detection, ranking, RAG/LLM), and production infrastructure. Drive technical strategy, cross-functional initiatives, experimentation, and mentor junior scientists while balancing latency, scalability, reliability, and business impact.

Leads advanced analytics initiatives supporting strategic planning, equity tracking, forecasting, budgeting, staffing, and resource allocation. Builds predictive models, simulations, and decision-support tools using education and enterprise data. Presents insights to executive and board-level audiences, partners with academic, operational, and finance teams, maintains reproducible workflows and data governance compliance, collaborates on data quality, and mentors analysts.

Develop predictive and statistical models to forecast turbine downtime, detect anomalies, and identify equipment degradation. Build P&L and operational dashboards, structure SCADA and maintenance data, classify outage events, support root-cause analysis, and recommend reliability improvements. Collaborate with engineering, operations, and asset management while communicating insights to technical and executive stakeholders.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Design, deploy, and manage machine learning models supporting client acquisition, retention, and reactivation. Develop Next Best Action, CRM, paid media, and personalization solutions using LLMs and traditional ML. Design and interpret experiments, analyze large-scale datasets, communicate recommendations to stakeholders, and collaborate with product, design, and engineering teams on product and infrastructure roadmaps.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Build and deploy machine learning, NLP, and LLM-powered solutions at scale. Improve production model performance, reliability, and accuracy; develop datasets, metrics, tools, and experimentation frameworks; and use data to influence product strategy. Partner with Product, Engineering, and business stakeholders to drive AI innovation, while serving as a technical thought leader and mentoring other data scientists.

Own the design and build of a data-quality and assurance function for a large-scale pricing intelligence data lake. Responsibilities include architecting observability, defining quality standards, building anomaly detection and health scoring, instrumenting pipelines, answering customer data-quality questions, and tracing issues to root causes across collection, processing, and source changes.