• Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms. • Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases (e.g., misconduct detection, market abuse, trade manipulation, insider communication). • Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities • Data annotation and quality review • Exploratory data analysis and model fail state analysis • Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards. • Client/prospect guidance in machine learning model and analytic fine-tuning/development processes • Provide guidance to junior team members on model development and EDA • Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team’s tooling, technique and procedures • Continued self-led personal development

Role Description The Sr. Director, Data Science & Advanced Analytics is responsible for defining and leading DataSpring's enterprise data science and advanced analytics strategy. This leadership role will focus on developing predictive models, machine learning solutions, and scalable analytics platforms to enable data-informed decisions across the organization. The Senior Director will build and lead a high-performing team that transforms provider and member data into actionable intelligence, supports business innovation, and drives measurable outcomes. - Define and execute a roadmap for enterprise data science that aligns with CAQH’s mission, data strategy, and product portfolio. - Identify key opportunities for predictive modeling, machine learning, and optimization to support provider and member data initiatives. - Lead the development of scalable models, algorithms, and decision-support tools to improve operations, data quality, and customer engagement. - Establish best practices for model development, validation, monitoring, and continuous improvement. - Guide the implementation of a robust, cloud-native analytics environment that enables rapid experimentation and insight generation. - Drive the development of reusable data products, features, and frameworks that scale across use cases. - Champion MLOps, automation, and reproducibility for production-grade model deployment and monitoring. - Lead the development of analytics dashboards, KPIs, and visualizations that empower business units to make data-informed decisions. - Partner with stakeholders to translate complex analytical outputs into business value, actionable insights, and measurable outcomes. - Standardize and govern enterprise-wide analytics metrics and methodologies to ensure consistency and reliability. - Work closely with Data Engineering, Architecture, and Governance teams to ensure data science solutions are interoperable, secure, and aligned with CAQH’s enterprise data ecosystem. - Collaborate with product, operations, and growth teams to embed analytics into workflows and external-facing solutions. - Partner with external vendors, research institutions, and data providers to expand modeling capabilities and data assets. - Recruit, lead, and mentor a high-performing team of data scientists, ML engineers, and analysts. - Foster a culture of curiosity, innovation, and continuous learning through coaching, technical leadership, and performance management. - Build team capacity and maturity across advanced analytics, statistical modeling, and AI/ML disciplines. Qualifications - Proven ability to lead enterprise data science strategy and deliver actionable insights that drive business impact. - Strong expertise in statistical modeling, machine learning, optimization, and data mining techniques. - Proficiency in Python, R, SQL, Spark, and data science frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). - Experience deploying models in cloud environments (Azure preferred) using MLOps tools and practices. - Exceptional communication and stakeholder management skills, with the ability to explain complex models and methods to non-technical audiences. - Deep understanding of BI, KPI frameworks, and performance measurement. - Familiarity with data governance and compliance frameworks (e.g., HIPAA, HITRUST, GDPR). - Knowledge of healthcare data and standards (e.g., FHIR, HL7, X12) is a strong plus. Requirements - 10+ years of experience in data science, analytics, or applied statistics, including 5+ years in a senior leadership role. - Demonstrated success in building and leading high-performing analytics or data science teams. - Proven track record in deploying machine learning models in production environments with measurable business outcomes. - Experience working with large, complex datasets, preferably in healthcare, health tech, or life sciences. - Bachelor’s degree in computer science, data science, statistics, applied mathematics, or a related field required. - Master’s or PhD in a quantitative discipline preferred. - Certifications in machine learning, data science, or cloud platforms (e.g., Azure) are a plus. Benefits - Competitive compensation and a comprehensive benefits package for full-time employees. - Medical, dental, and vision coverage. - 401(k) with company contributions and matching. - Paid parental leave. - Tuition assistance. - Generous paid time off. - Commitment to investing in our people and supporting professional growth over time. Company Description DataSpring is the trusted data connector at the core of healthcare. For more than 25 years, we have powered the industry with the largest and most complete healthcare data foundation in the U.S., including more than 4.8 million provider data records sourced directly from providers and member data representing 75% of covered lives supplied by health plans. By improving how essential information flows across the system, DataSpring helps healthcare operate more efficiently, accurately, and with greater confidence.

• Independently perform data analytics (ranging from data analysis, regression to machine learning) • Present complex concepts in a format digestible by a diverse audience across the organization • Build and improve the development of metrics, reports and analysis into BI dashboards that support a wide array of banking businesses • Manage ongoing data analytics processes, document calculations, code, and adhere to proper risk and compliance practices • Trouble shoot issues, suggest alternative approaches or controls to mitigate risks/gaps identified • Develop and design data-driven solutions in the areas of client insights and segmentation, sales & marketing lead generation, performance analysis and product analytics • Partner across the functions, including the line of businesses, IT, Data Office, and/or third-party vendors to co-build the data foundation and analytics delivery infrastructure for the action-driven insights and reporting • Assume end-to-end responsibilities on key initiatives assigned, and leverage partnership to achieve the shared vision

• Design and improve graph-based approaches for identity resolution across devices, households, and identifiers which improve match quality, coverage, and stability across a rapidly changing identity landscape. • Use Scala, Spark, SQL, and cloud-native tools to analyze large identity datasets, build models, and productionize data science workflows. • Contribute production-grade code to shared repositories, using strong engineering practices to build clear, scalable, and maintainable systems. • Define validation strategies and measure model performance and business impact on targeting, measurement, and attribution. • Help shape the team’s approach to identity science and partner across Engineering, Product, and Analytics to deliver production-ready solutions. • Leverage LLMs, AI editors (Cursor, Copilot, Claude Code), and agentic workflows to accelerate research, prototyping, documentation, testing, and iteration. • Apply privacy-by-design principles to ensure identity science work is auditable, compliant, and aligned with governance standards.