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Marcus Bell

Senior Data Analyst

marcus.bell@example.com +1 555 0142 Chicago, United States linkedin.com/in/example-profile example.com/portfolio

tech-stack --list

TechnicalSQL · Python (pandas) · Statistical hypothesis testing

ToolsLooker · dbt · BigQuery

Soft skillsExplaining findings · Stakeholder management · Training colleagues

summary

Five years of decision-support analytics in retail and subscription businesses. Experienced in churn prediction, pricing tests and cohort analysis. I care about analysis that ends in a measurable action rather than a report nobody opens.

work-experience

Senior Data Analyst

January 2022 – Present

Subscription retail business · Chicago

  • Built a churn prediction model; targeted campaigns took monthly churn from 6.4% to 4.1%.
  • Consolidated 38 scattered reports into 6 dashboards; weekly preparation fell from 14 hours to 3.
  • Designed a pricing A/B test that lifted mid-tier conversion 11 points and quarterly revenue 8%.

Data Analyst

March 2020 – December 2021

Multi-store retailer · Denver

  • Automated stock-turn analysis across 220 stores; dead stock value fell 23% by year end.
  • Moved 30 Excel reports into SQL, removing a recurring source of manual error.
  • Ran SQL training for product and marketing teams; ad hoc requests dropped 45%.

Reporting Analyst

February 2019 – February 2020

Logistics company · Denver

  • Built the delivery performance dashboard; on-time delivery improved 8 points in six months.
  • Wrote the validation layer that resolved inconsistencies in the daily sales pipeline.
  • Cut order cancellation from 7.2% to 3.4% through a carrier integration project.

education

September 2014 – May 2018

University of Illinois

BSc, Statistics · 3.7

September 2019 – June 2020

Northwestern University

Certificate Programme, Data Science

projects

Churn Early Warning System

A model producing a risk score 30 days ahead from behavioural signals. Accuracy 81%; retention in the treated segment improved 14 points.

languages

  • English · Native
  • Spanish · Proficient
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Engineering Flow

Monospace metadata and a tech-stack block; a developer aesthetic

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Monospace metadata and a tech-stack block; a developer aesthetic. The tech stack gathers into its own block at the top, with dates and locations aligned in tabular figures. Experience bullets are tightly spaced, written for technical results.

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