Alex Ilyukh

Turning raw data into decisions worth trusting.

Data Analyst — Northbrook, IL

I turn messy data into clear, decision-ready analysis using Python, SQL, Power BI, and statistical modeling — with hands-on ETL and cloud experience in Databricks and AWS, grounded in a background in sales and CRM data.

See the Portfolio Download resume

Foundation

The base layer — the languages, tools, and coursework that everything downstream gets built on.

Languages & analysis

  • Python
  • SQL
  • R
  • Statistical analysis
  • Machine learning
  • Object-Oriented Programming (OOP)
  • Statistical modeling
  • Applied machine learning
  • Supervised learning
  • Logistic regression
  • Decision tree learning
  • Probability & statistics
  • Data cleansing
  • ggplot2
  • R Markdown

Data engineering & cloud

  • Databricks
  • Apache Spark
  • PySpark
  • Delta Lake
  • Lakehouse architecture
  • Unity Catalog
  • Databricks CLI
  • ETL pipelines (medallion architecture)
  • Data pipelines
  • Data modeling
  • Database design
  • AWS (S3, EC2)
  • AWS Glue
  • Amazon DynamoDB
  • Amazon CloudWatch
  • IAM
  • Serverless computing
  • Cloud security
  • BigQuery

Visualization & reporting

  • Power BI
  • Tableau
  • Excel (Pivot Tables, VLOOKUP/XLOOKUP)
  • Google Sheets
  • KPI reporting
  • Dashboard design
  • Interactive data visualization
  • Business intelligence
  • Data-driven decision-making

Machine learning & emerging tech

  • Federated learning
  • PyTorch
  • TensorFlow
  • Flower
  • Homomorphic encryption

Business tools

  • Salesforce CRM
  • Business analysis
  • Workflow management
  • Data ethics
  • Cross-functional collaboration
  • Stakeholder communication
  • Bilingual — English, Russian

Certifications

Education

  • Lake Forest College
    B.A. Communication — Minor: Legal Studies & Philosophy, 2019
  • Oakton Community College
    Associate in Liberal Arts, 2016

Employment History

Nov 2024 – Present

Analytics and Development Intern

GCA Research & Consulting Services, Inc. — Remote

Supporting early-stage proprietary product development through research, analysis, and structured problem-solving in a confidential environment, while building hands-on capability in analytics, ETL pipelines, and cloud architecture.

Aug 2023 – Mar 2024

Sales Associate

Sleep Number — Deerfield, IL

Maintained Indigo CRM sales and customer-interaction data and used Power BI dashboards to monitor activity, pipeline performance, and daily sales strategy.

Apr 2021 – Feb 2023

Inside Sales Representative

Quill (Staples Inc.) — Lincolnshire, IL

Built operational dashboards to track productivity KPIs and forecast performance trends using Salesforce data. Conducted variance analysis against sales targets, contributing to a 33% improvement in efficiency.

Jan 2019 – May 2019

Marketing Intern

Kaplan Law Offices — Northbrook, IL

Analyzed SEO keyword and website performance with Google Analytics to improve search rankings, and tracked weekly marketing metrics for stakeholder reporting.

May 2018 – Aug 2018

Legal Intern

Bikbova Law Office — Northbrook, IL

Built structured country-condition index reports from geopolitical and legal research, and organized case files extracted from legal documents and client interviews.

Jun 2013 – Sep 2020

Event Operations & Design Specialist

Neky Design — Northbrook, IL

Coordinated logistics and cross-functional teams across multiple concurrent events under tight deadlines.

Portfolio

The refined output — work built and shipped end to end, from raw data to a finding someone could act on.

Rent Responsibly

2026

A location-aware rental affordability tool: combines commute isochrones, live rental listings, and full household financial modeling (tax, debt, expenses, fuel) to evaluate whether a move is sustainable under single-income and co-signing scenarios.

  • Python
  • Pandas / GeoPandas
  • Mapbox API
  • Geospatial analysis
  • Financial modeling
View on GitHub

Relocation Feasibility Analysis

2026

Models income, housing costs, debt, and living expenses against real government data — HUD housing figures and Tax Foundation tax data — to answer a concrete question: is a specific relocation actually affordable, not just possible.

  • Python
  • Polars
  • HUD data
  • Tax Foundation data
View on GitHub

Monte Carlo Job Offer Comparison

2026

Runs a 50,000-trial Monte Carlo simulation to compare compensation structures across job offers, turning uncertain, volume-based commission math into a distribution of likely outcomes rather than a single guess.

  • Python
  • NumPy
  • Monte Carlo simulation
View on GitHub

Clipboard Health Case Study

2025

Analyzed CMS Payroll-Based Journal staffing data and provider records across 14,000+ U.S. nursing homes to identify staffing cost inefficiencies, model resource demand, and deliver strategic recommendations to sales leadership.

  • Python
  • Machine learning
  • CMS PBJ data
View Presentation

Customer Call-Time Optimization Dashboard

2022

Built a Google Sheets-based planner at Quill for tracking real-time call-duration data, designed to double as a live dashboard for the team — contributing to a 33% gain in efficiency.

  • Google Sheets
  • Dashboard design
View Dashboard Get Your Own Copy
How to use this dashboard
  1. Make your own copy of the sheet, then duplicate the "Call Time Planner" tab. Do all of your daily edits on the Copy of Call Time Planner tab — this keeps the original tab clean and makes resetting for the next day easy.
  2. If your shift starts at a different time, edit cell G2 under the TOD column.
  3. Set your goal in cell O8 — clicking it reveals a dropdown. Selecting 3:45:00 sets an average call-time target of 0:08:02 per 15-minute segment; selecting 4:30:00 raises that target to 0:09:39.
  4. If you're participating in a Flex Time program (working a bit extra Mon–Thu to leave early on Friday), add Flex Time starting with the first item in the list, adding more as your policy permits. Each addition lowers your required goal per 15-minute segment.
  5. If you're leaving early, start unchecking the L column from the bottom. Also make sure no Flex Time is checked.
  6. Block out breaks, lunch, and meetings — those segments highlight in yellow to mark their start and end.
  7. Throughout the day, once a 15-minute segment is complete, edit that row's Adjusted cell with your actual call time. Delete the existing contents and type your time with no equals sign, in 00:00:00 format (e.g. 00:05:00). The next row automatically recalculates the call-time goal needed to stay on track.

Google Data Analytics Capstone

2025

Integrated Divvy bike-share ridership data with hourly weather data to uncover behavioral trends and develop data-driven recommendations for growing annual membership enrollment.

  • R
  • RStudio
View Presentation