Product Analytics Insights Leader

Fractal Analytics•Minnesota, MN

21h•$150,000 - $200,000

About The Position

Fractal is seeking a Product Analytics & Insights Leader to own analytics strategy, the measurement operating model, and executive performance storytelling for an enterprise AI platform focused on employee experience. The role connects product behavior, employee experience, and business outcomes to show where the platform creates value, where it falls short, and what leadership should do next. It is a decision-shaping leadership position, not a reporting role, with influence over roadmap and investment priorities and responsibility for leading analysts and directing AI-first analytics systems. The leader reports to the Director, Product Management for the AI product team and works closely with product management, engineering, UX, research, and business stakeholders. They establish success metrics, KPI definitions, baselines, and review cadences; lead AI-assisted analysis and automated dashboards; evaluate launches and impact; integrate qualitative and quantitative user signals; and turn evidence into recommendations on roadmap sequencing, prioritization, and investment. They own concise executive presentations, reports, and visualizations that communicate impact, drivers, evidence confidence, and recommended decisions. They also lead and develop analytics staff and establish responsible review standards for AI-powered workflows and outputs. The preferred location is Minneapolis, Minnesota; relocation support is available for the right candidate. The stated salary range is $150,000–$200,000, with potential eligibility for a discretionary bonus.

Requirements

8+ years of experience in analytics, product analytics, business analysis, consulting, or research, including substantial experience interpreting large datasets and translating findings into product or business decisions.
3+ years leading analytics programs or teams, including setting analytical priorities, establishing measurement practices, coaching analysts, and delivering cross-functional outcomes.
Ability to use hands-on SQL to validate complex queries, investigate data quality issues, and critically evaluate AI-generated analysis.
Demonstrated experience shaping KPI frameworks, evaluating product performance, and developing evidence-based prioritization or business-case recommendations.
Proven ability to build clear executive narratives and influence cross-functional decisions without relying on reporting authority.
Sound understanding of analytical limitations and uncertainty, and of the human verification required for AI-assisted analysis.
Bachelor's degree in analytics, business, information systems, computer science, engineering, statistics, economics, or a related field.

Nice To Haves

Experience designing and scaling automated workflows or agents using Copilot Studio, Power Automate, or equivalent tools, and delivering decision-ready dashboards in Power BI, Looker, or Tableau.
Depth in experimentation and KPI frameworks, user research, journey mapping, or customer/employee experience measurement, including integration of behavioral data with qualitative research.
Experience with digital assistants, agents, or machine-learning-enabled products, ideally in complex enterprise product, technology, or transformation initiatives.
Experience developing analytics staff and building durable, cross-functional measurement practices.
Commitment to building diverse, inclusive teams.

Responsibilities

Define measurement strategy, including success metrics, KPI definitions, baselines, and review cadences that connect product objectives to adoption, engagement, retention, satisfaction, business performance, and observability metrics.
Lead AI-assisted analysis and automated executive dashboards with reliable refreshes and verified narratives, focusing analyst effort on exceptions, anomalies, root causes, and decision implications.
Evaluate launches using ongoing instrumentation and, where appropriate, experimentation; distinguish observed change from credible evidence of impact and recommend corrective action.
Integrate employee feedback, surveys, support trends, experience metrics, and product behavior to identify unmet needs and friction, partnering with UX and research on journey improvements.
Translate evidence into recommendations on sequencing, feature prioritization, and investment trade-offs; develop and challenge business cases; use targeted market and competitive research to test strategic assumptions; and assess realized value after launch.
Produce concise executive presentations, reports, and visualizations explaining impact, underlying drivers, confidence in the evidence, and recommended decisions, including unfavorable findings.
Lead and develop analytics staff, set priorities, coach analytical reasoning and executive communication, and plan team capacity.
Replace manual analytics and reporting with reusable AI workflows, prompts, and agents, using review standards for source traceability, SQL validation, reconciliation, and narrative accuracy.