• Collaborate closely with the Snowflake engineering team to define, spec, and validate data requirements for models and AI applications • Serve as the translation layer between data engineering and AI/ML teams, ensuring data is structured, clean, and optimized for downstream use • Enable and support agentic AI workflows by designing and maintaining the data layers that power agent memory, context retrieval, and tool-use • Build and maintain data models and transformation logic on top of Snowflake to support predictive and generative AI use cases • Identify and resolve data quality issues in collaboration with the engineering team • Partner with marketing science and analytics teams to ensure data assets are accessible and model-ready • Automate and streamline data validation processes to improve reliability of AI-facing data pipelines

• Build and deploy demand forecasting and pipeline prediction models that project revenue outcomes at the campaign, segment, and market level • Develop and maintain lead scoring, audience propensity, and opportunity scoring models to sharpen targeting and prioritize sales and marketing effort • Analyze and model the relationship between marketing investment, channel performance, and revenue outcomes, connecting spend to growth • Identify high-value market segments and audiences through data modeling to inform how and where we compete • Develop personalization models and frameworks that enable tailored customer experiences across channels, from dynamic content and offers to next-best-action recommendations • Own and shape the revenue data ecosystem, ensuring data integrity, influencing how data is captured, and making it model-ready across systems • Collaborate with Marketing and Sales teams to define forecasting methodologies and embed models into operational workflows • Translate complex model outputs into clear, actionable narratives that drive strategic decisions at the executive level

Role Description The Data Scientist will assist with data analytics and statistical modeling. In this role, the Data Scientist will collect, analyze, and interpret complex data sets to assist the end customer’s financial investigations. - Develop and execute complex database queries to extract and manipulate both structured and unstructured datasets, streamlining routine business processes and automating report generation. - Design and optimize business analytics workflows tailored for a US Government organization, enhancing the analysis and visualization of operational datasets. - Contribute to the development of investigative analysis capabilities and provide technical support for systems, ensuring data integrity and accessibility for analysis. - Collaborate as part of a small team while also managing independent tasks related to help desk support, efficiently handling ticketing data flows, generating insightful reports, and visualizing key performance indicators through dashboards for enhanced organizational decision-making. - Other duties as assigned. Qualifications - Bachelor’s degree from an accredited college or university - 3+ years of experience - Data analytics - Data wrangling - Experience working with financial crimes a plus - Public Trust required - Advanced proficiency in SQL required - Advanced proficiency in data wrangling with at least one of the following: R, Python, SAS/SAS Macro Language, Visual Basic - Self-starter and problem solver - Excellent communication skills - verbal and written - Flexibility and attention to detail - Ability to follow verbal and written instructions - Data visualization using tools such as Power BI, Excel, R, or Python - Microsoft Power Query Benefits - Broad range of benefits offered to team members. - Positive and supportive culture encouraging team members to do their best work. - Well-being programs and flexibility to make daily choices for a healthy work-life balance. - Opportunities for professional development and hands-on experience.

• Business Insight & Problem Definition: Partner closely with marketing and product teams to diagnose business challenges and define appropriate data metrics, analyses, and interpretations to drive strategic decisions. • Statistical Modeling: Define requirements for and lead the creation, monitoring, and iterative improvement of key statistical models used for marketing spend optimization and product growth. • Narrative & Visualization: Design clear, unambiguous narratives and data visualizations to communicate model outputs and analysis results to broad, non-technical audiences. • Rapid Prototyping: Independently prototype and test new insights, moving from hypothesis to rigorous validation with minimal mentorship. • Technical Consultation: Reason effectively about the strengths and weaknesses of different modeling approaches, selecting and iterating on combinations that suit specific marketing contexts.