Top Remote Data Science Jobs in Houston, TX

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 product, growth, AI/automation, or partnerships.

Embed in a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive outcomes by recommending and executing data-driven actions end-to-end.

Own analytics for an embedded business unit end-to-end: define problems, set metrics, build reports, run analyses and experiments, surface insights, and drive data-informed decisions and outcomes with product, growth, AI, or partnerships teams.

Embed with a business unit to define problems, design experiments, build metrics and reports, run analyses using SQL/Python, surface actionable insights, and drive data-informed decisions end-to-end.

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, and partnerships.

Develop and maintain balance sheet forecasting models and related analytics. Perform data mining, segmentation, and statistical testing; monitor model performance and propose recalibration or redevelopment. Analyze mortgage/MSR prepayment, deposit and interest-rate behavior. Collaborate with Model Risk, Market Risk, and Treasury for validations, CCAR/DFAST, and balance sheet optimization using QRM and BlackRock Aladdin.

Develop and implement ML models (regression, classification, forecasting, optimization); perform feature engineering, model training, validation, and evaluation; support experiments (A/B tests), monitor model performance; prepare structured and semi-structured data; write clean Python and SQL; collaborate with Data/ML Engineering for deployment and communicate results to stakeholders.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Apply advanced analytics, statistical modeling, and machine learning to build and operationalize predictive models for pricing, underwriting, claims, and operations. Partner with business, product, and engineering teams to integrate models, monitor performance, improve data pipelines, and deliver dashboards and insights that inform strategic decisions.

The Risk Data Scientist will develop adaptive decisioning models for risk assessment in financial transactions, focusing on fraud detection and compliance, while managing model governance and deployment in a regulated environment.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

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.

Build and deploy NLP solutions: perform EDA on text, design and evaluate models (classification, NER, information extraction), develop pipelines from regex to transformers, apply generative AI and prompt engineering, write production-grade Python, and collaborate with ML engineering to deploy and monitor scalable cloud-based solutions.

Develop and deploy statistical and machine learning models to analyze customer behavior across marketing and digital channels. Design and evaluate experiments/A-B tests, apply causal inference, build scalable Python/SQL pipelines, query Redshift, use SageMaker for production models, maintain version-controlled workflows, and present insights to cross-functional stakeholders.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Design and implement reinforcement learning models and sequential behavioral models to optimize collections and treatment strategies. Build scalable ML pipelines (Databricks), run simulations and offline policy evaluation, and collaborate with stakeholders to deploy adaptive decisioning systems under uncertainty.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Build and deploy predictive and time-series models using HR and clinical data (Oracle HCM, GCP) to forecast staffing, identify flight-risk employees, analyze pay/burnout relationships, and create interactive dashboards and analyses to inform HR strategy and operational workforce decisions.

Design, build, and deploy machine learning and generative AI models (including LLMs and text-to-image). Analyze large datasets, run A/B and statistical tests, collaborate with data engineering and engineering teams, and create dashboards/visualizations to communicate insights. Optimize data pipelines and model performance to support data-driven decisions.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.