Director, Business Intelligence (Data and Analytics) - Hybrid

Wolters Kluwer•Minneapolis, MN

7d•$137,900 - $246,350•Hybrid

About The Position

The Director – Data Science and Analytics is accountable for the delivery, quality, and impact of data science, analytics, and AI capabilities across multiple products and platforms. This role focuses on leveraging data-driven strategies, drive operational efficiency, informs decision-making, and positions the DnA organization for continued growth and competitive advantage. The Director serves as the leading authority for data analytics projects, while partnering with Product, Engineering, IT, and Business leaders to translate regulatory, customer, and market needs into scalable, machine‑first solutions.

Requirements

Extensive experience in data science, analytics, or applied AI, including senior leadership roles
Proven ability to lead largescale, production product/ systems in complex environments
Deep intuition for modelling trade‑offs, data quality limitations, and real‑world failure modes
Experience applying data science in regulated, compliance‑driven, or high‑stakes business contexts
Demonstrated capability to translate strategy into scalable, data‑driven execution
Strong communication skills across executive, product, and technical audiences
Track record of building high‑performing teams and senior technical leaders
Ownership mindset with focus on long‑term outcomes rather than short‑term activity
Bachelor’s degree in engineering, Data Science, Mathematics, or a related field
15+ years of extensive progressive leadership experience in data science, analytics, or AI‑enabled organizations
Proven experience defining and executing enterprise‑scale data science or AI strategies
Track record of operating in executive environments with senior stakeholder accountability

Nice To Haves

Advanced degree preferred (MBA)

Responsibilities

Own the data and analytics strategy for DnA team in FCC, spanning applied machine learning, analytics, and GenAI
Provide scientific and technical leadership for production data science systems operating at enterprise scale
Own the quality, confidence, and integrity of data and AI outputs used across customer‑facing and regulatory products
Establish standards for explainability, traceability, validation, and audit‑readiness of AI‑driven outcomes
Drive organizational understanding of data, entities, signals, taxonomies, and domain behavior
Ensure decision science solutions balance accuracy, scale, cost, and risk while remaining commercially viable
Enable product differentiation and velocity through reusable analytics, AI platforms, and shared capabilities
Support data monetization and external distribution through APIs and strategic partner integrations
Define and govern enterprise‑grade data quality, monitoring, and AI lifecycle practices
Partner closely with Product, Engineering, and IT leadership to align data science outcomes with business and compliance priorities
Build, mentor, and scale senior data science talent, setting the technical and cultural bar for the organization.