• Design, develop, and deploy causal inference models (e.g., uplift modeling, synthetic control, double machine learning) to understand the true drivers of customer loyalty and measure the incremental impact of marketing interventions. • Build robust machine-learning-based forecasting and predictive models for customer lifetime evaluation (LTV) and customer churn. • Establish foundational data modeling frameworks for a brand-new Business Unit, transforming raw transactional data into scalable features. • Analyze complex customer behavior, purchase patterns, and engagement metrics to build strategies for direct revenue generation. • Perform large-scale data extraction, transformation, and analysis using SQL. • Partner with marketing, product, and business teams to understand loyalty requirements and translate business problems into analytical solutions. • Present model insights and recommendations to senior client stakeholders, clearly communicating the difference between correlation and causation. • Lead workshops and customer analytics strategy discussions with clients. • Implement and operationalize models in cloud environments.

• Ship the models that power personalized in-app experiences — predicting what each member is likely to want next based on real-time behavior, family context, and engagement history. • Detect meaningful patterns in product usage data — communication, scheduling, and activity signals — and build the models that turn those patterns into in-app suggestions, alerts, and guidance for families. • Help replace fixed-placement UI logic with algorithmic decisioning that adapts every time someone opens the app. • Build the churn, retention, and activation models that help Product understand which behaviors predict long-term value and which interventions are worth shipping. • Develop predictive segmentation, propensity, and uplift models, refreshed continuously through automated pipelines so the rest of the business is always acting on current signal. • Partner with Finance on cohort-level subscriber and revenue forecasting, LTV by acquisition source, and sensitivity analysis on the assumptions that matter most. • Build pricing, promo, and refund-risk models that quantify the financial impact of monetization decisions before we ship them. • Train, deploy, and monitor models in Databricks and AWS, using Claude Code as your primary engineering interface. • When agentic delivery makes sense, wrap models in managed agents that surface predictions into the workflows stakeholders already use. • Move the team from "what happened" to "what would happen if" — uplift modeling, geo-tests, and A/B analysis grounded in proper causal framing.

• Ship the models that power personalized in-app experiences — predicting what each member is likely to want next based on real-time behavior, family context, and engagement history. • Detect meaningful patterns in product usage data — communication, scheduling, and activity signals — and build the models that turn those patterns into in-app suggestions, alerts, and guidance for families. • Help replace fixed-placement UI logic with algorithmic decisioning that adapts every time someone opens the app. • Build the churn, retention, and activation models that help Product understand which behaviors predict long-term value and which interventions are worth shipping. • Develop predictive segmentation, propensity, and uplift models, refreshed continuously through automated pipelines so the rest of the business is always acting on current signal. • Partner with Finance on cohort-level subscriber and revenue forecasting, LTV by acquisition source, and sensitivity analysis on the assumptions that matter most. • Build pricing, promo, and refund-risk models that quantify the financial impact of monetization decisions before we ship them. • Train, deploy, and monitor models in Databricks and AWS, using Claude Code as your primary engineering interface. • When agentic delivery makes sense, wrap models in managed agents that surface predictions into the workflows stakeholders already use. • Move the team from "what happened" to "what would happen if" — uplift modeling, geo-tests, and A/B analysis grounded in proper causal framing.

Role Description We are seeking an inquisitive Data Scientist to join our team, leveraging deep expertise in Artificial Intelligence and a keen understanding of complex Defense Acquisition challenges. Your primary focus will be to work with our proprietary and commercial datasets to find critical connections, derive actionable knowledge, and identify potential issues that help our government clients make more informed decisions. Your role will involve designing, developing, and rapidly deploying scalable AI solutions that transform these insights into mission-critical capabilities. You will have the opportunity to expand and grow data-driven research across Govini, leading new areas to apply advanced analytics to drive strategic business and national security results. As part of our team, you must be an AI expert with a strong command of data science fundamentals and a proven ability to bring data-driven solutions to life within the defense ecosystem. In order to do this job well, you must be a highly organized problem-solver and possess excellent oral and written communication skills, capable of translating complex AI concepts for both technical and non-technical audiences. You are independent, driven, and motivated to jump in and roll up your sleeves to get the job done. You lead by influence and motivation. You have a passion for great work and nothing less than your best will do. You share our intolerance of mediocrity. You’re uber-smart, challenged by figuring things out and producing simple, impactful AI solutions to complex defense problems. This role is a full-time position located out of our office in Arlington, VA, Pittsburgh, PA, or Remote. This role may require up to 50% travel. Scope of Responsibilities - Develop AI/ML/DS systems to address defense problems. - Develop new AI-based stack of technologies for particular customer needs. - Development of evaluation solutions for AI deployments. - Lead the end-to-end execution of AI projects, ensuring alignment with strategic objectives and timely delivery. - Work closely with partner teams, including AI research, data engineering, and software development, to deliver robust AI systems. - Collaborate with the AI leadership to clearly define product and applications scope, translating high-level requirements into actionable technical plans and delivering AI platforms and solutions. - Drive the development of scalable, AI-based platforms designed to support and power solutions for a diverse range of customers within the defense ecosystem. - Champion the introduction of new AI innovations. Stay current with the rapidly evolving fields of Artificial Intelligence, Large Language Models (LLMs), and agentic systems, and actively transfer this knowledge and best practices for implementation across the team. - Mentor and guide other team members, fostering a culture of technical excellence, continuous learning, and high-impact work. - Create agentic AI systems, using AI orchestrators and particular AI reasoning capabilities as tools. Qualifications - U.S. Citizenship is required. - Oversee the full lifecycle of AI projects within Govini AI ecosystem. - Understanding of coding AI (such as Claude Code) and experience how to use it to scale up AI team deliveries. - Ensure timely project delivery and strategic goal alignment. - Collaborate with cross-functional teams (AI Research, Data Engineering, Software Development). - Define robust software architecture for AI systems and project roadmaps. - Translate high-level AI system requirements into actionable technical plans. - Deliver production-ready AI solutions integrated with clients data platforms and workflows. - Drive the implementation of scalable AI/ML platforms. - Maintain deep expertise in cutting-edge AI, including LLMs and agentic systems. Train models, build systems based on AI innovations. - Champion innovation and best practices across the organization. - Mentor and coach team members. - Foster a high-performing culture of technical excellence and continuous growth. - Demonstrate strong National Security domain knowledge. Desired Skills - A strong publication record in the top conferences in one of the AI, ML NLP, decision science areas. - Deep knowledge of LLM and Agentic system evaluation, experience in building evaluation systems. - Experience in LLM posttraining or fine tuning. - Experience with MLOps practices, including deployment, monitoring, and management of large-scale models in cloud environments (e.g., GCP, AWS, Azure). - Track record of successfully transitioning AI research prototypes into robust, production-grade enterprise solutions. Benefits - We firmly believe that past performance is the best indicator of future performance. - If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you. Company Description Govini transforms Defense Acquisition from an outdated manual process to a software-driven strategic advantage for the United States. Our flagship product, Ark, supports Supply Chain, Science & Technology, Production, Sustainment, Logistics, and Modernization teams with AI-enabled Applications and best-in-class data to more rapidly imagine, develop, and field the capabilities we need. Today, the national security community and every branch of the military rely on Govini to enable faster and more informed Acquisition decisions.