About the role: We are seeking a Senior Data Scientist with deep expertise in financial fraud detection and claim abuse. This role will lead the design and implementation of advanced analytics and machine learning solutions to combat evolving fraud and dispute schemes. The ideal candidate will have a proven track record of delivering high-impact models, mentoring junior team members, and influencing strategic decisions across the organization. Responsibilities include: Strategic Leadership • Drive the roadmap for internal AI & ML initiatives, aligning with business objectives and regulatory requirements. • Serve as a subject matter expert on fraud analytics, advising leadership on emerging threats and mitigation strategies. Advanced Modeling • Architect and deploy cutting-edge machine learning and AI models to maximize fraud recovery, behavioral analysis, and predictive fraud prevention. • Optimize models for scalability, real-time performance, and minimal false positives. Data Innovation • Identify new data sources and develop advanced feature engineering techniques to enhance fraud detection capabilities. • Lead research into novel algorithms, including graph analytics and deep learning approaches. Collaboration & Influence • Partner with Legal, Leadership, and Engineering teams to integrate solutions into enterprise systems. • Communicate complex technical concepts to non-technical stakeholders and influence decision-making. Mentorship • Guide and mentor junior data scientists, fostering a culture of innovation and continuous learning. Governance & Compliance • Ensure models adhere to regulatory standards (OCC, PCI DSS) and ethical AI practices. Required Qualifications: - 7+ years of experience in data science, with at least 3 years focused on fraud detection or financial risk analytics. - Expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and advanced statistical modeling. - Strong proficiency in Python, SQL, and Snowflake - Deep understanding of financial systems, payment networks, and fraud typologies. - Experience deploying models in production environments and working with real-time streaming platforms. Preferred Qualifications: - Familiarity with graph databases and network analysis for fraud rings. - Knowledge of cloud platforms (AWS) and MLOps best practices. - Exceptional communication and leadership skills.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a member of the HOPE Team, the Data Lead will partner with the current team to take joint ownership of the HOPE team’s data-related processes and lead in developing and using innovative evaluations, practices, and tools. - Respond to time-sensitive and high-level requests for data related to the Strategic Framework to Prevent and End Homelessness. - Socialize data definitions and develop a shared vision for quality data. - Establish reporting policies and procedures to ensure timely and clean data. - Produce products that facilitate data-driven decision making. - Promote the importance of creating and using data for continuous quality improvement methods and system change efforts. Qualifications - Bachelor’s degree in statistics, data or quantitative analysis, public policy, business, public administration, sociology, or directly related field. - Five years of professional program administration and/or analytical experience in area of assignment. - Two years of supervisory experience. - Significant experience in statistical analyses, data reporting from complex systems, and building tools/dashboards in business intelligence software, like Power BI or Tableau. - Knowledge and experience with service delivery documentation, like HUD standards, HIPAA/Confidentiality standards, utilization review, and data management. Requirements - Refine HOPE team’s data strategy by examining gaps, opportunities, and best practices from other cities. - Manage high-leverage projects and reports that are time-sensitive and essential to the Strategic Framework to Prevent and End Homelessness. - Present major findings to key stakeholder groups inclusive of City leaders, homelessness providers, partners in other sectors, and the public at large. - Ensure the by-name dataset is accurate, timely, and comprehensive by creating data insights and developing policies and procedures. - Design programmatic evaluations that reveal focus areas leading to robust conversations on areas of improvement. - Collaborate with the HOPE team to review strategic planning towards system-level problems. - Develop internal data tools that support staff in efficiently processing the data to better understand their work towards key measures. - Problem-solve data issues from a systems perspective by leveraging knowledge of HMIS to identify trends or anomalies in data. - Inform policies and procedures to clear the path for stronger data infrastructure. - Evangelize the use of data for improvement, advocacy, and storytelling. - Engage with Built for Zero to develop a strong understanding of quality data definitions. - Analyze data and produce data products including monthly inflow/outflow reports and Built for Zero metrics. - Identify and develop plans to fill community data capacity/capability gaps through training and advocating for data resources. Company Description

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). • Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. • Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. • Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights. • Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization. • Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers. • Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

• Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). • Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. • Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. • Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights. • Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization. • Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers. • Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.