Intelligent Optimization Lead, Continuous Improvement

Microsoft

Confirmed live 2 days ago High trust

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $101,800–$193,800 / yr
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Closes: Mar 1, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $195k

This role $148k

$85k most similar roles pay here $255k

This role pays less than 80% of similar roles. Most pay $157,200–$232,037 — the shaded band above. At the midpoint, this role pays about $148k versus about $195k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 598 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 586 roles with salary data.

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At a glance

TL;DR · Intelligent Optimization Lead, Continuous Improvement

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The Intelligent Optimization Lead, Continuous Improvement joins the Frontier Transformation team to ensure AI value compounds over time rather than plateauing after initial deployment. This role focuses on establishing and running the Intelligent Optimization Framework as a permanent operating rhythm grounded in kaizen and strong facilitation. You will be responsible for redesigning workflows, solving last-mile problems by embedding new ways of working into standard work, and managing agent drift while refining prompt libraries and enablement materials. Key responsibilities include analyzing Copilot Dashboard signals to identify usage gaps and translating those metrics into prioritized improvements. To succeed, you must utilize skills in Lean or Six Sigma methodologies, stakeholder management, and quality assurance for production systems. The role addresses the challenge of making AI adoption sustainable by creating durable execution cadences that remain after the initial engagement concludes.

Skills

AI Microsoft Copilot Viva Insights Kaizen Lean Six Sigma Process Improvement Operational Excellence Quality Assurance Standard Work

What you'll do

Establish and run a continuous improvement framework as a permanent operating rhythm based on kaizen principles.
Redesign and rerun workflows through structured cycles where measurement data informs subsequent improvements.
Embed new ways of working into standard work, clear ownership, and disciplined execution cadences to ensure changes stick.
Analyze Copilot Dashboards and other metrics to identify usage gaps and translate them into prioritized actions.
Refine prompt libraries, enablement materials, and agent configurations based on real-time usage data.
Detect and correct agent drift while retiring or reworking low-value AI agents to focus resources on high-ROI tasks.
Create reusable improvement patterns and playbooks to increase the quality and speed of subsequent cycles.
Transfer continuous improvement tools and ownership to customer teams to ensure sustainability after the engagement ends.

What we're looking for

A Master's degree in Business, Engineering, Operations, Information Systems, or a related field is required.
Candidates must have 4 years of experience with a Bachelor's degree or 6 years of experience without a Master's in continuous improvement or transformation delivery.
Experience with kaizen, Lean, or comparable methodologies and facilitating improvement events is preferred.
Proven ability to establish standard work, ownership models, and execution cadences to make change durable.
Ability to interpret operational and adoption metrics to drive prioritized improvements.
Strong facilitation and stakeholder management skills across both business and technical teams.
Experience managing AI or automation solutions in production, including agent drift detection and lifecycle management.
Familiarity with Microsoft Copilot adoption and the Copilot Dashboard in Viva Insights is preferred.