Data Innovation Partner Senior

The University of Kansas Health System•Lenexa, KS

1d•Onsite

About The Position

The Data Innovation Partner Senior is a senior individual contributor who delivers significant, enterprise-visible work within the Data Innovation Partner function — advancing what analytics can do through emerging technology and innovation, and scaling how the enterprise uses analytics through strategic growth in adoption, footprint, and value realization. Together, these accountabilities position data as a foundation for every meaningful decision. This role actively contributes to the enterprise's AI journey, applying responsible AI, machine learning, and emerging analytics practices in initiatives that expand what data can do for patient care, operations, and strategic decision-making. The Senior Partner independently owns meaningful initiatives from intake through delivery, applying established methodologies, frameworks, and standards set by Lead and Principal Partners. They translate strategy into execution — designing solutions, running experiments and driving adoption activities, measuring outcomes, and sharing lessons learned. As an experienced practitioner, the Senior contributes to team methodology, mentors less experienced Partners, and helps their peer group deliver consistently. The Senior partners with senior stakeholders and executive-adjacent audiences to translate business problems into analytical solutions. The Senior also embeds data integrity considerations into their work, partnering with the Data Integrity team to uphold enterprise standards. Success in this role is measured by initiative outcomes, quality of work, and growth of the practitioners around them.

Requirements

Bachelors Degree Business/Innovation, Data Science, Computer Science, Engineering, Health Administration, Organizational Development, Adult Learning, or Communications, or a related field — or equivalent years of experience.
7 or more years in analytics, coding development, analytics enablement/adoption, or organizational change work tied to data and technology, or a related technical role.
5 or more years in each of the following: applying ROI analysis, performance indicators, and impact metrics to evaluate program or initiative outcomes and inform prioritization decisions; analytics, business intelligence, digital transformation, or analytics adoption/change enablement, with demonstrated impact beyond a single project or team; supporting performance improvement, process optimization, business transformation, or project/change management initiatives
3 or more years in each of the following: applying business intelligence tools (e.g., Power BI, Tableau, Epic SlicerDicer) to enable analytics adoption, decision-making, and user education; using portfolio and work management tools (e.g., ServiceNow, Azure DevOps, Jira) for intake, routing, documentation, prioritization, and tracking; developing adoption and enablement metrics (e.g., usage telemetry, training impact, satisfaction) to measure analytics ROI, applying value measurement frameworks, performance indicators, and impact metrics at the project or program level; serving as a recognized subject matter contributor in innovation, analytics adoption, or a related specialty — trusted to represent the team on their area of expertise, contributing to methodology and standards shaped by senior peers
2 or more years in each of the following: owning significant initiatives end-to-end, with quantified outcomes (e.g., delivery milestones met, adoption/utilization achieved, measurable value delivered, framework refinement, capability improvement); and where possible, at least one AI, machine learning, or emerging technology initiative; working with data quality, lineage, metadata, or data governance concepts as part of analytics, innovation, or adoption initiatives, in partnership with integrity/governance functions; mentoring or supporting less experienced practitioners through coaching, pairing, work review, or knowledge sharing

Nice To Haves

Master's Degree Business Administration, Health Administration, Data Analytics, Organizational Development, or related field
Epic Cogito Fundamentals (COG170) certification
Epic Cogito Project Manager (COG300) certification
LEAN certification
Complete Epic SQL I coursework
Complete Epic SQL II coursework
Complete advanced role-aligned learning paths in technology and people leadership (e.g., DataCamp tracks, Epic fundamentals, change management micro credentials), with at least 50 hours annually within 1 Year
Innovation Track: Agile Scrum Master certification
Advisory & Adoption Track: APMG Change Management Foundation or equivalent recognized change-adoption credential within 1 Year
0–2 years SQL experience in a relational database, or an equivalent combination of education and experience
Demonstrated proficiency in SQL, Python or R, and cloud data platforms (e.g., Azure Data Lake, Databricks, Synapse, Snowflake) — with the ability to design data models, transformations, and integrations at scale.
Foundational credential or coursework in AI, machine learning, or responsible AI practices (e.g., Microsoft AI Fundamentals, Google AI Essentials, Databricks fundamentals, healthcare AI ethics coursework).
2+ years applying AI, machine learning, or generative AI capabilities to analytics initiatives — including designing prompts, integrating AI-assisted analysis, evaluating model outputs, and understanding responsible AI considerations.
3+ years working with healthcare data ecosystems (clinical, operational, financial, regulatory) — with demonstrated understanding of Epic, Caboodle, or comparable healthcare data platforms, and awareness of HIPAA, CMS, and clinical data implications.
Advanced technical credential aligned to team assignment or specialty (e.g., Azure AI Engineer, Databricks ML Professional, Tableau/Power BI advanced certifications, advanced change/adoption practitioner).
Healthcare environment experience.
Foundational understanding of data observability, metadata management, and lineage practices.
Familiarity with Epic analytics ecosystems (Cogito, Caboodle, Radar).
Demonstrated impact beyond a single team (contributing to enterprise standards, adoption playbooks, or maturing communities of practice).
Experience participating in governance councils, technical review boards, or portfolio prioritization forums.
Formal change-management or product ownership training (e.g., CCMP, APMG, PROSCI, CSPO/CPO).
3+ years partnering with senior stakeholders and executive-adjacent audiences (VPs, Directors, Physician Leaders, operational executives) to translate business problems into analytical solutions and influence decision-making.

Responsibilities

Independently own significant initiatives from intake through delivery within the team's scope, applying methodologies, frameworks, and standards set by senior peers.
Design and deliver innovation experiments and adoption activities that produce measurable outcomes (learning, ROI, utilization, value realized).
Contribute to the design and delivery of AI, machine learning, and emerging technology initiatives — applying enterprise standards for responsible AI, evaluation criteria, and integration patterns established by Principal Partners.
Participate actively in team forums (standups, planning, retrospectives); contribute to continuous improvement of team practices.
Anticipate risks and blockers within initiatives and raise them proactively to Leads/Principals for coordination.
Contribute to standardizing workflows and reducing cycle time by identifying and sharing repeatable patterns from delivery work.
Partner with stakeholders across the organization to translate business needs into innovation experiments and adoption plans, aligned with enterprise strategy and governance standards.
Coordinate with peer Senior Partners across the function to share methodology, avoid duplication, and align on approach.
Apply analytical frameworks and evaluation standards to initiatives, inclusive of AI/ML considerations for scale and risk, generative AI use cases and guardrails, and analytics adoption considerations for ROI, user enablement, and value realization.
Apply responsible AI standards — including data privacy, model transparency, bias awareness, HIPAA/CMS alignment, and human oversight expectations — to AI/ML work.
Contribute to enterprise programs and initiatives; provide input on approach and methodology from a practitioner perspective.
Identify integrity, risk, or readiness considerations within work and escalate to Lead/Principal or Data Integrity partners where needed.
Build and maintain recognized depth on a specialty area within innovation, analytics adoption, or a related domain; serve as the trusted go-to for the peer group on that specialty.
Contribute to team methodology, playbooks, and standards by sharing what works, refining approaches based on delivery experience, and offering perspective in team methodology discussions.
Apply established value measurement approaches to evaluate initiatives; contribute lessons learned to inform framework refinement by senior peers.
Stay current with AI, machine learning, and emerging analytics developments relevant to the specialty area; bring learnings back to the team.
Embed data quality, lineage, and integrity considerations into initiatives from the start, partnering with the Data Integrity team on standards and enterprise practices.
Mentor less experienced Partners (Standard, Associate) through coaching, pairing, work review, and knowledge sharing.
Represent initiatives and specialty in cross-functional planning, working sessions, and stakeholder forums.
Partner with peer Seniors across the function to align on practitioner-level coordination and share lessons across teams.
Model a collaborative, evidence-driven culture that reflects enterprise values; contribute to psychological safety and openness within the team.
Support Lead and Principal Partners in preparing communications, artifacts, and materials for executive and governance forums.
Contribute to enterprise data literacy and AI literacy by translating specialty depth into accessible guidance for clinicians, operational leaders, and analytics peers.
Champion continuous improvement by questioning assumptions on initiatives, running structured retrospectives, and applying learnings forward.
Must be able to perform the professional, clinical and or technical competencies of the assigned unit or department.