Senior Data Analyst at Microsoft · Reno, NV

Greg Chedwick

Domain agnostic: I turn messy data into metrics, dashboards, and automation people actually use.

Senior analytics professional with 20+ years in data analytics and 6+ years leading analytics initiatives, spanning software licensing, mortgage and consumer lending, advertising, supply chain, and freight. I design scalable metrics, build dashboards that drive decisions, and automate the manual work that quietly eats teams alive — most recently with AI agents built in Copilot Studio. I do my best work in ambiguous 0-to-1 territory with minimal oversight.

Portrait of Greg Chedwick

Impact, quantified

Every figure below traces to a specific project in the career history — no round numbers without a story behind them.

  • 4,300+

    Hours automated per year

    Anniversary and mid-term ordering workflows, enabling 3x business scaling in two years

  • $106M

    On-time renewal lift

    Driven by compliance analytics and automated deep-dive reporting

  • $3.2B

    Agreement portfolio in view

    Power BI dashboards giving stakeholders live visibility into portfolio health

  • $31M

    Cost reduction

    Data-driven business cases built with DMAIC and Agile methodologies

  • $5.7B

    Revenue enabled

    Process and system improvements substantiated through analytics

  • $60B+

    Portfolio remediated

    Loan modification campaigns supporting $25B+ in government programs

23 years, three employers

Each bar spans one employer. Select a role to read what it involved — or read them all below, no clicking required.

  1. Microsoft

    Jun 2015 – PresentReno, NV

    • Senior Data AnalystFusion Development, Operations Service Center
      • Engineered scalable licensing data models, automated workflows, and developed Power Apps that automated anniversary and mid-term ordering — saving 4,300+ hours annually and enabling 3x business scaling over two years.
      • Developed compliance analytics metrics and Power BI dashboards giving visibility into a $3.2B+ agreement portfolio, driving a $106M increase in on-time renewals.
      • Designed agreement complexity models to identify bottlenecks, enabling targeted root-cause investigations and scalable automation for ad-hoc requests.
      • Built custom AI automation with Copilot Studio to enable stakeholder self-service, integrate disparate data sources, and accelerate insight delivery.
    • Business Analytics SpecialistBusiness Process & Analytics, Commercial Ops
      • Led global analytics backlog prioritization using Cost of Delay / Weighted Shortest Job First within a SAFe framework, ensuring timely deployment of high-impact BI across software licensing, advertising, and supply chain.
      • Partnered with cross-functional stakeholders to define requirements and ship scalable dashboards that informed decisions and supported new program launches.
      • Developed automated reporting pipelines and self-service tools across digital attach, hardware compliance, search, and advertising.
    • Business Operations AnalystProcess Management, Commercial Ops
      • Crafted data-driven business cases using DMAIC and Agile methodologies, substantiating improvements that cut costs by $31M, reduced AR exposure by $400M, and enabled $5.7B in revenue.
      • Engineered BI solutions and monitoring dashboards to track outcomes of process and system improvements.
  2. Bank of America

    Aug 2010 – May 2015Reno, NV

    • Vice President, Consumer Products Strategic ManagerLoan Loss Mitigation and Portfolio Analytics
      • Led the analytics team behind loan modification campaigns, remediating a $60B+ portfolio and supporting $25B+ in government programs through advanced BI and reporting.
  3. Charles Schwab Bank

    Aug 2003 – Aug 2010Reno, NV

    • Finance Manager, Bank FinanceBank Finance
      • Spearheaded development of the loan database and analytics infrastructure, improving reporting, lead generation, and portfolio management across Finance, Credit, and Compliance.

Skills, by years in the seat

Hover any tool in the ticker for what I've done with it. Below, bar length is hands-on years on one shared scale — two entries are marked as recent rather than deep, because being straight about that matters more than a longer bar.

Data & BI

  • SQL15+ yrs

    Complex querying, data modeling, large-scale analysis

  • KQL5+ yrs

    Kusto queries over log and telemetry data

  • Power BI & DAX10+ yrs

    Interactive dashboards, reporting, visualization

  • SSMS & VS Code10+ yrs

    Data modeling, analysis, AI-assisted coding with Claude Code and Copilot CLI

Data Engineering

  • Microsoft Fabric & SSIS10+ yrs

    ETL pipelines, SQL databases, lakehouses

  • Azure DevOps10+ yrs

    Plan, build, test, and deploy solutions

  • Azure Platform Services5+ yrs

    Function Apps, Logic Apps, Data Factory, serverless automation

  • PythonGrowing

    Data manipulation, scripting, automation

Automation & AI

  • Power Automate & Power Apps7+ yrs

    Workflow automation and self-service tools

  • Microsoft Copilot StudioRecent

    AI agents, low-code intelligent automation

Education

  • Master of Business Administration

    University of Nevada, Reno · 2013

    GPA 4.0 · Data Resource Management, Information & Communication Technology, Strategic Management

  • B.S. Business Administration

    California State University, East Bay · 1998

    Minor in Computer Science

Projects

Things I build outside work to stay sharp on the tools. Each one ships end to end — data in, decision out — rather than stopping at a notebook.

  • Netflix Ads Analytics

    Shipped

    An end-to-end analytics project simulating what an ad platform team needs to decide where ad inventory is worth buying — built as if for Netflix’s ad-supported tier.

    • Cleaned and merged 32,000+ Netflix titles with 2025–26 global top-500 viewership data
    • Designed a custom Ad Opportunity Score weighting hours viewed, sustained relevance, ratings, and recency
    • Shipped an interactive Power BI dashboard alongside the notebook
    • Python
    • Pandas
    • Matplotlib
    • Jupyter
    • Power BI
    • Git
  • This Site

    Shipped

    The portfolio you’re reading. Built as a static site so the resume itself renders as live data visualization rather than a PDF nobody opens.

    • Career history and impact metrics render from a single typed data file
    • Charts validated for colorblind separation and contrast in both light and dark mode
    • Deploys automatically to GitHub Pages on every push to main
    • Astro
    • TypeScript
    • SVG
    • GitHub Actions

Netflix Ad Opportunity Score

Which titles are worth buying ad inventory against? I scored all 500 titles in the 2025–26 global top 500 by blending how much they were actually watched with how long they stayed relevant. These charts run on the real output of that analysis — not screenshots.

Top Titles by Opportunity Score

Score blends hours viewed, sustained ranking, rating, and recency.

  1. KPop Demon Hunters- 8992025 · 41 wks · 992M hrs
  2. Back in Action- 2302025 · 7 wks · 254M hrs
  3. Frankenstein- 2032015 · 5 wks · 224M hrs
  4. Happy Gilmore 2- 2022025 · 5 wks · 224M hrs
  5. The Rip- 1962025 · 7 wks · 216M hrs
  6. War Machine- 1842017 · 4 wks · 203M hrs
  7. STRAW- 1732025 · 5 wks · 191M hrs
  8. The Life List- 1562025 · 6 wks · 172M hrs
  9. Exterritorial- 1542025 · 15 wks · 169M hrs
  10. Wake Up Dead Man: A Knives Out Mystery- 1432025 · 5 wks · 157M hrs
  11. The Great Flood- 1362025 · 7 wks · 149M hrs
  12. Havoc- 1292025 · 6 wks · 142M hrs

Hover or focus a bar for the underlying numbers. A bar that fades at its end runs past the axis — the axis stops above the runner-up so the rest stay comparable.

How the Score Is Weighted

Hours viewed dominates deliberately — it is the closest available proxy for realised ad impressions. The rest adjust for staying power and quality.

  • Total hours viewed75%Strongest predictor of ad inventory value
  • Weeks in Top 1013%Measures sustained cultural relevance
  • Vote average8%Higher-rated titles hold attention longer
  • Release year4%Newer content attracts more viewers
Rank by

Both charts respond to these.

Genres by Average Score

Averaged across every ranked title carrying that genre. Title counts vary — War is a small, high-performing set.

  1. War- 40.77 titles · 313M hrs
  2. Romance- 26.724 titles · 697M hrs
  3. Science Fiction- 26.533 titles · 953M hrs
  4. Comedy- 23.881 titles · 2.1B hrs
  5. Drama- 21.974 titles · 1.8B hrs
  6. Thriller- 21.272 titles · 1.7B hrs
  7. Action- 19.797 titles · 2.1B hrs
  8. Horror- 19.420 titles · 420M hrs
Data notes and known limits

Genre coverage is partial: only 21 of the top 60titles carry genre metadata, because most 2025–26 chart-toppers are newer than the catalogue they join against. That is why there is no genre filter on the titles chart — it would silently hide two-thirds of the data. The genre panel aggregates across all500 ranked titles, where coverage is high enough to average meaningfully.

KPop Demon Hunters is a genuine outlier at roughly four times the second-place score, which compresses everything below it. It is left in rather than trimmed, because the whole point of the metric is to surface exactly that kind of title.

Data generated 2026-07-26 from the project's processed output.

Open to analytics leadership roles

Based in Reno, NV, working remotely across time zones. If you are building something in the 0-to-1 space and need someone to make the data legible, I would like to hear about it.