Role Description Navarro Research and Engineering is recruiting a Senior Data Scientist / AI Engineer (3878). This is a remote position. Citizenship is required. We are seeking a Senior Data Scientist / AI Engineer to design, develop, deploy, and maintain machine learning and generative AI solutions within a government environment. This role will support both locally hosted AI systems and cloud-based AI services within Microsoft Azure Government, including Azure AI Foundry and related Azure AI services. The ideal candidate has hands-on experience building production AI systems, deploying and operating open-source large language models (LLMs), implementing secure MLOps practices, and developing AI applications that meet government security and compliance requirements. Qualifications - Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or related field. - Master's degree preferred. - 5+ years of experience in data science, machine learning, AI engineering, or related fields. - 2+ years of experience deploying and operating production AI/ML systems. - Experience supporting secure government, defense, or regulated environments preferred. Requirements - Strong knowledge of supervised and unsupervised learning techniques. - Experience with model development, evaluation, and optimization. - Statistical analysis and experimental design experience. - Proficiency in Python and common ML frameworks. - Experience deploying and operating open-source LLMs. - Experience with Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure Kubernetes Service (AKS), Azure Storage and Data Services, Azure Identity and Access Management. - Experience with Docker, Kubernetes, GPU-based inference systems, vLLM, Ollama, TGI, or similar inference platforms. - Understanding of model quantization and performance optimization techniques. - Experience with SQL and relational databases, data warehousing concepts, ETL/ELT pipeline development, vector databases, and semantic search platforms. - Git-based development workflows, REST APIs and microservices, CI/CD pipelines, Infrastructure-as-Code concepts. Benefits - Health Care Plan (Medical, Dental & Vision) - Retirement Plan (401k) - Life Insurance (Basic, Voluntary & AD&D) - Paid Time Off (Vacation & Public Holidays) - Short Term & Long Term Disability Success Criteria Within the first 12 months, the selected candidate will: - Deploy and support production AI solutions in Azure Government. - Establish repeatable MLOps processes for AI model deployment and maintenance. - Deploy and manage secure local/open-source LLM environments. - Develop mission-focused AI applications leveraging RAG and agentic workflows. - Improve operational efficiency through automation and advanced analytics. Due to the nature of the government contract requirements and/or clearances requirements, US citizenship is required. Navarro is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, race, religion, color, national origin, age, disability, veteran’s status, or any classification protected by applicable state or local law. EEO Employer/Vet/Disabled

• Own and evolve Comfrt’s measurement framework alongside partners across MMM & MTA (Northbeam), incrementality (Workmagic), and experimentation • Lead Marketing Mix Modeling (MMM) initiatives in partnership with measurement partners and internal stakeholders • Design and evaluate geo tests, holdouts, lift studies, and quasi-experimental frameworks • Quantify true incremental impact across marketing channels, campaigns, creators, and growth initiatives • Identify saturation points, diminishing returns, and marginal efficiency curves across paid and organic channels • Continuously improve model accuracy, interpretability, and decision-making reliability • Develop models and analytical frameworks that improve CAC efficiency, LTV forecasting, contribution margin visibility, and retention economics • Analyze customer cohorts to identify drivers of high-value and durable customer behavior • Partner with Growth and Finance teams on forecasting, budget allocation, and investment planning • Help distinguish short-term performance gains from long-term brand and customer value creation • Build frameworks that measure the relationship between acquisition strategy, retention quality, and enterprise value creation • Build scalable analytical systems and production-grade models using Python and SQL • Partner with Data Engineering & Data Analytics & Insights to improve data quality, metric governance, and experimentation infrastructure • Standardize and maintain trusted business metrics including CAC, LTV, MER, contribution margin, retention, and incremental ROI • Collaborate with Engineering and BI teams to operationalize models into dashboards, workflows, and reporting systems • Ensure measurement systems remain scalable, reliable, and actionable as the business grows • Translate complex analyses and modeling outputs into clear, actionable business recommendations • Influence executive decision-making around growth investments, profitability, and long-term business quality • Partner cross-functionally across Growth, Finance, Product, Data Engineering and Technology teams. • Help establish a culture of rigorous, data-informed decision-making across Comfrt

• Lead the design, implementation, and deployment of ML/DL algorithms. • Apply quantitative analysis and an insightful presentation of data to see beyond the numbers and understand how our users interact with our growth product. • Apply data mining techniques, perform statistical analysis, and build high-quality predictive models. • Meet the business vision and strategy in accordance with the product innovation backlog. • Collaborate with the product and engineering teams to deliver usable and informative analytical interfaces.

• Forecasting model ownership • Build and iterate production grade demand and volume forecasts • Evaluate models with backtesting and real-world performance • Maintain stability with retraining, drift monitoring, and failure handling • Improve models based on operational outcomes • Define input data contracts for optimization models including capacity, constraints, and demand • Transform raw data into model-ready features and constraints • Validate solver outputs and identify infeasibility, constraint conflicts, and scaling issues • Trace issues back to data assumptions and constraint design • Convert outputs into usable schedules and recommendations • Compare model output to operational reality and explain gaps • Run scenario analyses under different constraints • Improve pipelines that support forecasting and optimization quality • Test assumptions against real-world variation • Explain models and outputs to non-technical users including operators and clients • Translate tradeoffs between accuracy, feasibility, and constraints • Deliver clear recommendations tied to business outcomes • Participate in client conversations and defend model behavior