Top Senior Data Science Jobs in Albuquerque, NM

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

As a Staff Data Scientist, you will lead product strategy by building predictive models, optimizing matching algorithms, and guiding product innovations in a consumer tech environment.

As Staff Data Scientist, you will drive advanced analytics to optimize sales performance, build predictive models, and develop insights for decision-making across the GTM organization.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

Customer-facing data scientist advising customers on experiment design, statistical methodology, and experimentation architecture; running and analyzing A/B tests; building scalable experimentation programs; creating repeatable frameworks and technical enablement; partnering with Sales, Solutions Engineering, Product, and Engineering to drive customer success.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Develop a tool to enhance signature management capabilities and integrate diverse data holdings, leveraging AI/ML solutions to meet operational needs.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

As a Staff Data Scientist, you will lead experimentation strategies, apply advanced causal inference techniques, and guide analytical insights to drive product and business decisions.

Lead technical roadmap and delivery of ML and GenAI capabilities for retail pricing, demand forecasting, promotion effectiveness, and markdown recommendations. Architect GenAI agents and set model/MLOps standards, mentor distributed data science teams, partner with product and engineering, and present AI strategy to executives and customers.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Build and maintain large commercial datasets and predictive/forecasting models (BigQuery, dbt, SQL, Python). Design causal and experimental analyses to measure promotion impact and price elasticity. Partner with commercial, finance, and executive stakeholders to inform pricing, distribution, and investment. Deploy LLMs and AI agents to classify unstructured data and build tools that democratize data access.

The Staff Data Scientist will design and optimize deep learning models for fraud detection, mentor peers, and lead data science initiatives.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Senior Data Scientist oversees data collection and preparation, designs study protocols, leads report development, trains junior employees, and manages project workstreams while collaborating with team members to optimize business performance.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

The Senior Data Scientist will integrate data from various sources, analyze web performance, and provide insights for strategic decision-making. Responsibilities include developing automated systems for content coverage, tracking metrics, and designing experiments to assess new initiatives' impact.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

Lead development, validation, deployment, and quality control of advanced analytics and ML solutions for fraud detection, waste and abuse, investigative analysis, and program integrity. Provide technical leadership, translate stakeholder requirements, mentor team members, and ensure models are explainable, defensible, and production-ready.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.