Senior Director, Data Science & Advanced Analytics

DataSpring

5h•Remote

About The Position

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.

Requirements

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.
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.

Nice To Haves

Master’s or PhD in a quantitative discipline preferred.
Certifications in machine learning, data science, or cloud platforms (e.g., Azure) are a plus

Responsibilities

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.