Operations & Strategy

I approach every business like an owner.

Business operations and strategy analyst focused on creating value, knowing what’s worth measuring, and making decisions that build on each other.

See selected work Get in touch

About

I’m Ifeoma Ozoede, a business operations and strategy analyst based in Chicago, working at the seam between the analytical and operational sides of how companies grow. I studied Business Information Systems, and Information Systems. The pairing wasn’t a master plan, but it clarified something for me: I’m far more interested in what the numbers mean than in the machinery that produces them. I’m comfortable working in Python and R, but what pulls me in is the layer on top — how value gets created, what’s worth measuring, and what a business should actually do differently as a result.

What drives me is ownership in the real sense — understanding how things actually create value, how they get built and managed well, and how a good decision today turns into something bigger later.

Education

MS, Information SystemsDePaul University · 2026 · GPA 3.80
BSc, Business Information SystemsOregon State University · 2023

Involvement

Private Equity & Venture CapitalStudent member · DePaul
Information Systems Student OrganizationMember · DePaul

Experience

Business Office Assistant — DePaul Family & Community Services

Nov 2024 — Jun 2026

The operational side of how an organization actually runs — money, compliance, and the workflows that keep everything moving.

  • Kept HIPAA-compliant operational and financial data accurate (99%+) so clinical and administrative teams could decide on numbers they could trust.
  • Ran monthly invoicing and payment tracking, keeping billing and revenue operations clean and audit-ready.
  • Found where interdepartmental workflows broke down and rewrote the process guidelines to fix them.

Product Strategy Intern — One Life Startup

Jun 2025 — Jul 2025

Strategy work at the earliest, messiest stage — working directly with a founder on a pre-launch product.

  • Analyzed user needs, market trends, and competitors for a pre-launch wellness app, and turned it into a clear read on where to focus first.
  • Helped prioritize features and shape early go-to-market calls alongside the founder and the product and marketing teams.
  • Translated research into product briefs the team actually used to plan the launch.

Data Inventory Specialist — UST (Contract)

Dec 2023 — Aug 2024

Operations and data governance at real scale, supporting Intel’s asset management across global teams.

  • Managed and analyzed 1,000+ assets daily across global teams in Jira and ServiceNow, keeping inventory accurate and auditable.
  • Ran weekly compliance and inventory audits, catching discrepancies before audit deadlines.
  • Held 100% SLA compliance on support tickets, keeping workflows from stalling.

Project Assistant — Oregon State University

Sep 2022 — Mar 2023

Early grounding in keeping projects on track — deliverables, timelines, and the people who need to stay in the loop.

Projects · Selected Work

BI-Driven Customer Personalization — Business Case

Group · Strategy · Financial modeling

An e-commerce company sitting on customer data it couldn’t use — browsing, CRM, and purchases trapped in three disconnected systems. My piece was the planning and financial backbone of the business case to fix it: a roughly $275K first-year budget broken out line by line, an 18-month phased rollout, a risk register with mitigations, and the assumptions behind a six-figure investment decision — plus the dimensional data model underneath the reporting layer.

Read the business case →

Chicago Airbnb Price Analysis

Solo · R · Multiple regression

If you’re pricing an Airbnb in Chicago, two things actually move the needle — what kind of space it is and where it sits — while the review count hosts agonize over barely matters. I got there by building up five regression models across ~6,300 listings, layering in location, reviews, and availability, then log-transforming price to handle the skew from luxury outliers. Room type and neighborhood were the real levers. The point isn’t the R² — it’s knowing which factors deserve a host’s attention, and being honest about the ~60% the data can’t explain.

Read the full analysis →

Renewable Energy & Weather Analysis

Group · R · Regularized regression

Renewable energy output turns out to be genuinely predictable from weather — and solar conditions dominate everything else. On a dataset of 100,000+ observations, the response was heavily skewed and the weather variables were tangled together, so I square-root-transformed the response and used regularized regression (Ridge, LASSO, Elastic Net), which is built for correlated predictors like these. Tuned with cross-validation, Elastic Net predicted about 85% of the variation in energy change, with solar irradiance the standout driver — the kind of signal that makes supply easier to plan around.

Read the full study →

Skills

Analysis & Modeling

  • R
  • Regression (Ridge / LASSO / Elastic Net)
  • Exploratory data analysis

Data & Tools

  • Excel (advanced)
  • SQL
  • Python
  • pandas
  • Jira
  • ServiceNow

Business & Operations

  • Business case development
  • Budget & risk analysis
  • Process improvement
  • Stakeholder coordination

Contact

Always happy to talk operations, strategy, or a good idea someone’s trying to build.

Reach me here: sylviaifeomaozoede@gmail.com

Chicago, IL LinkedIn GitHub