Title: Technical Analytics Manager / Lead Data Scientist (Fraud Analytics & Investigative Support) Location: Fairfax (REMOTE), Virginia, United States Job Description: Title:Technical Analytics Manager / Lead Data Scientist (Fraud Analytics & Investigative Support) ID:2143 Description Location: Remote (Occasional Travel May Be Required) Clearance: Ability to obtain and maintain a Public Trust Position Overview Praescient Analytics is seeking a highly skilled Technical Analytics Manager / Lead Data Scientist to provide technical leadership for a federal fraud analytics and investigative support program. This individual will serve as the technical authority responsible for designing innovative analytic approaches, developing advanced fraud detection models, leading model validation and quality assurance activities, and ensuring the successful delivery of reliable, defensible, and repeatable analytic solutions supporting federal oversight organizations. The ideal candidate is a hands-on technical leader who combines deep data science expertise with strong leadership skills. They will guide multidisciplinary technical teams through the full analytics lifecycle, from ideation and data exploration to model development, testing, deployment, and continuous improvement, while mentoring technical staff and collaborating closely with Government stakeholders, investigators, and program leadership. Key Responsibilities - Lead the design, development, testing, validation, deployment, and continuous improvement of advanced fraud detection and investigative analytics solutions. - Develop innovative analytic approaches to identify fraud, waste, abuse, and mismanagement across large-scale federal benefit programs. - Design and implement analytic rules, machine learning models, artificial intelligence (AI) solutions, natural language processing (NLP), anomaly detection, entity resolution, graph analytics, link analysis, risk scoring, and other advanced analytic capabilities. - Lead technical teams responsible for model development, experimentation, quality assurance, documentation, and production deployment. - Perform hands-on development of analytic models using open-source programming languages, frameworks, and data science tools. - Conduct exploratory data analysis, feature engineering, data profiling, model evaluation, and performance optimization across complex datasets. - Establish and oversee rigorous quality control processes to ensure analytic outputs are accurate, reliable, repeatable, and fully documented prior to Government delivery. - Review technical work products, source code, analytic methodologies, and model outputs produced by contractor support teams. - Identify technical risks, recommend mitigation strategies, and ensure timely delivery of high-quality analytic products. - Collaborate with Project Managers, Data Engineers, Graph Data Scientists, Investigative Analysts, Forensic Accountants, and Government stakeholders throughout the project lifecycle. - Present analytic methodologies, technical findings, model performance, and recommendations to Government leadership, investigators, and oversight organizations. - Support Agile delivery through sprint planning, backlog refinement, technical demonstrations, and iterative model development. Required Qualifications - Must have experience with Fraud Analysis - Five (5) or more years of hands-on experience developing analytic rules and models for fraud detection use cases using leading-edge analytic tools and best practices. - Five (5) or more years of experience designing analytic approaches, managing model development and testing efforts, and conducting thorough quality control. - Demonstrated experience ideating innovative analytic use cases to detect and prevent fraud, waste, abuse, and mismanagement. - Five (5) or more years of experience tracking project progress, identifying technical risks, and delivering high-quality analytic solutions on schedule. - Five (5) or more years of experience reviewing contractor-developed analytic models, code, methodologies, and work products prior to final delivery. - Five (5) or more years of hands-on experience developing analytic rules and models using open-source programming languages and frameworks. - Strong written, verbal, presentation, and technical communication skills. - Demonstrated ability to lead technical teams while remaining actively engaged in hands-on analytics development. Preferred Qualifications Preference will be given to candidates with demonstrated experience in one or more of the following areas: - Developing fraud detection, fraud prevention, and program integrity analytics supporting pandemic relief, emergency assistance, grants, loans, healthcare, unemployment insurance, disaster relief, financial assistance, or other high-volume federal benefit programs. - Designing, developing, validating, deploying, and maintaining advanced analytic models utilizing machine learning, artificial intelligence (AI), natural language processing (NLP), anomaly detection, entity resolution, graph analytics, link analysis, knowledge graphs, risk scoring, or robotic process automation (RPA). - Applying open-source programming languages and frameworks such as Python, SQL, Spark, Pandas, Scikit-learn, TensorFlow, PyTorch, or comparable data science technologies. - Developing analytics within cloud-native environments utilizing Azure Databricks, Microsoft SQL Server, Microsoft Fabric, Azure Data Lake, Power BI, Neo4j, Git repositories, Lakehouse architectures, or enterprise data catalogs. - Working with large-scale public, non-public, commercial, financial, and law enforcement datasets to identify organized fraud rings, synthetic identities, duplicate entities, eligibility issues, and emerging fraud schemes. - Conducting exploratory data analysis, data profiling, feature engineering, model validation, performance testing, and quality assurance throughout the analytics lifecycle. - Supporting Offices of Inspector General (OIGs), law enforcement organizations, oversight agencies, or program integrity initiatives through advanced analytic solutions. - Developing reproducible analytic workflows that emphasize governance, documentation, transparency, explainability, and enterprise data management best practices. - Leading technical reviews, mentoring data scientists, and establishing quality standards for analytic products delivered to Government customers. What We're Looking For We're looking for a technical leader who enjoys solving complex fraud detection challenges while remaining actively involved in hands-on analytics development. The ideal candidate is equally comfortable writing code, designing machine learning models, reviewing technical work products, mentoring fellow data scientists, and briefing analytic findings to senior Government stakeholders. They combine innovative thinking with disciplined engineering practices to ensure every analytic solution is technically sound, operationally effective, and capable of supporting real-world investigative and oversight missions. What you can expect from us: - Real opportunity for career growth in an environment where your achievements will be celebrated - Constant collaboration with numerous teams to ensure client success - A team that respects and embraces your ideas and expertise - Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain - A workplace dedicated to supporting and bettering public safety and government agencies Benefits: - Competitive salary based on qualifications and experience - Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles) - 401(k) with company match - Travel & performance incentives - 3 weeks paid time off (plus Federal Holidays) - $5K annual training allowance - $500 book allowance - Tuition reimbursement program Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law. Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222-90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient’s employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information. US Citizenship Required Interested Candidates: Please forward your resume to recruiting@praescientanalytics.com and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.

• Collaborate with stakeholders to gather requirements and understand business needs • Design and develop interactive dashboards using Tableau • Optimize data models and queries using SQL • Ensure data accuracy and reliability in the dashboards • Create reports and visualizations that effectively communicate insights • Provide technical support and troubleshooting for dashboards • Complete other assigned projects and administrative tasks

• Support exploratory, descriptive and predictive analysis of user feedback data; • Contribute to building and evolving data and AI pipelines, including data ingestion, processing and insight generation; • Participate in the development, testing and evaluation of LLM-based solutions, including advanced fine-tuning and prompting, and implementation of RAG (Retrieval-Augmented Generation); • Assist in creating and experimenting with intelligent agents integrated with tools, APIs and knowledge bases; • Assist in defining and monitoring model quality and performance metrics; • Contribute best practices for development, documentation and critical evaluation of the limitations of AI solutions; • Collaborate on automating analytical processes to achieve scalability and efficiency.

Role Description As a Revenue Analytics Manager, you’ll be the go-to individual contributor for turning complex sales and go-to-market data into clear insights and tools that help leaders make better decisions. You’ll work with first order, SMB, and sales development and core sales performance metrics to build AI tools, analytics, and insights that Field Operations stakeholders can rely on. You’ll own projects end to end: partnering with Sales and Operations stakeholders to understand questions, managing projects, running analysis, and translating results into recommendations that drive tangible impact. You’ll work primarily with Claude, dbt, Snowflake, SQL, and Python to build insights that can drive the business. Deliverables will be platform agnostic where you will be asked to determine the right solution for the current need, be it a scalable data model, verified queries, agentic workflows, dashboard and reporting, or even ad hoc analysis via gsheets. You’ll collaborate closely with adjacent functions such as Pipeline, Sales Strategy, and Customer Success analytics. Over your first year, you’ll focus on establishing a strong analytics foundation for new business and first orders, delivering quick-win insights and scalable reporting, and becoming a trusted partner to leaders across the go-to-market organization. What you’ll do - Lead end-to-end first order and new business analytics work, from defining questions with stakeholders to building datasets, analysis, and clear recommendations that inform sales decisions. - Determine which legacy Tableau dashboards need to be maintained, which need to be updated to new reporting standards, and which can be migrated to AI-based solutions. - Drive deep analysis of first order pipeline, new business attainment, and other sales metrics using SQL and Python to uncover trends, risks, and opportunities. - Collaborate closely with Sales, Field Operations / RevOps, Finance, and Customer Success partners to translate business needs into scalable analytical solutions and tools. - Own the quality, structure, and usability of sales data in partnership with central data teams, ensuring consistency across Snowflake, dbt models, and other data sources where relevant. - Manage stakeholder expectations and project timelines, prioritizing requests and communicating trade-offs and outcomes in a clear, structured way. - Partner with Customer Success and broader go-to-market teams to align sales analytics with go-to-market and customer success metrics where needed. - Contribute to improving analytics processes, documentation, and standards so the team can work more efficiently and reuse high-impact analytical assets. Qualifications - Background working in sales analytics, with hands-on experience interpreting key sales metrics such as pipeline health, quota attainment, and forecast accuracy. - Proficiency with AI and business intelligence tools, with direct experience using Tableau or a similar BI platform and the ability to adapt to GitLab’s tooling and approach. - Practical skills in querying, and transforming data using SQL and Python, and comfort working with modern data warehouses and analytics environments. - Experience with Analytic Engineering and the ability to determine when a solution should be a view, a table, or a custom query to meet long term objectives. - Ability to manage projects and stakeholders, translating business questions into analytical plans and communicating findings to non-technical partners. - Experience supporting sales or go-to-market teams, with familiarity in areas such as new business development, pre-sales, marketing, or small and mid-market sales processes. - Exposure to tools and concepts such as dbt, Snowflake, or comparable technologies, and an understanding of data structures that support scalable reporting. - Clear, structured communication skills in an all-remote, asynchronous environment, including documenting work and collaborating across time zones. - Openness to learning GitLab’s product, data model, and ways of working, with transferable experience from sales, revenue, or field operations analytics roles. Benefits - Flexible Paid Time Off - Team Member Resource Groups - Equity Compensation & Employee Stock Purchase Plan - Growth and Development Fund - Parental Leave