I’m Zahra, and I work where data meets people.

I’m a Business Analytics student at the University of Geneva with a background in Industrial Engineering, fascinated by how people behave, decide, and interact, and how data and AI can make sense of it.

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Zahra Ghafghazi

About me

My background is in engineering and math, and I loved the math. But I was always just as drawn to people: how they behave, decide, and interact. After a coaching course, that pull only grew, so I went looking for a field where I could keep both sides: the analytical and the human.

These days, what pulls me in is the space between people and data: human behavior, decision-making, and how we interact with AI, with a real soft spot for marketing and management. I’m also high-energy and naturally curious, the kind of person who loves good food, new perspectives, and a good conversation with people.

Education

Where I’ve studied, and what I focused on.

Fall 2025 – Present

MSc, Business Analytics

University of Geneva, GSEM
Fall 2021 – Spring 2025

BSc, Industrial Engineering

Amirkabir University of Technology
GPA 18.2 / 20 · (3.86 / 4.0)
2018 – 2021

Diploma, Mathematics & Physics

SAMPAD, National Organization for Development of Exceptional Talents
GPA 19.84 / 20

Experience

Academic
Winter & Spring 2025

Teaching Assistant, Macroeconomics

Under Dr. Ehsan Hajizadeh’s supervision
Guided students and developed exercises for the course.
Fall 2024 – Spring 2025

Teaching Assistant, Entrepreneurship & Business Planning

Under Dr. Cyrus’s supervision
Prepared presentations and guided students on their projects.
April 2020 – May 2022

Mathematics Tutor

Tutored students in mathematics, including statistics and business-model concepts.
Industry
Dec 2023 – March 2025

Personal Coach

Delivered 85 hours of personalized coaching in academic achievement, skills growth, and business strategy.
Summer 2024

Internship: evaluating operational data to improve planning and decision-making

Food production factory
Improved efficiency through data analysis, lean thinking, and 5S.
Jan 2022 – Dec 2022

Educational Support

At North Star Success, Canada, led by Dr. Anari
Analyzed learning difficulties, suggested solutions, and shared progress updates.
Jan 2022 – Oct 2022

Customer Support Services

At North Star Success, Canada, led by Dr. Anari
Created educational content to answer audience questions and boost engagement.

Projects

A selection of my academic work, each with its goal, the tools I used, and what I took away.

Advanced Data-Driven Decision MakingSpring 2026

A marketing analytics course with Prof. Marcel Paulssen (GSEM), built around four applied case studies.

  • Case studies
    • Case Study 1: Experience curve (Heliotronics). Heliotronics, a solar-panel maker, needed to estimate future production costs before bidding on a Swiss installation project. I modeled its experience curve (Y = A·Xb) using a log-log transformation and linear regression, interpreted the learning rate, forecast unit cost, and added confidence intervals to recommend a bid price. The fit was strong (R² ≈ 0.95) with a ~10% learning rate, giving a forecast cost of about $697 per panel (95% CI roughly $606 to $802).
    • Case Study 2: Smartphone quality perceptions (factor analysis). Using survey data from 979 customers across 33 quality items, I ran exploratory factor analysis (KMO, Bartlett’s test, scree plot, parallel analysis, Promax rotation) to find the latent dimensions of perceived quality. I retained an 8-factor solution (30 items, ~74% of shared variance, all Cronbach’s α > 0.85). Ease of Use and Performance were the strongest drivers of willingness to pay and repurchase, and repurchase drivers differed by brand (Apple: serviceability, Samsung: durability, LG: material quality).
    • Case Study 3: Brand equity (SEM, Galeries Lafayette). Facing declining sales, the brand needed to know which perceptions drive loyalty. I combined exploratory and confirmatory factor analysis with structural equation modeling and path and mediation analysis on Likert survey data, linking brand-image perceptions to satisfaction, affective commitment, repurchase intention, and co-creation intention, then turned it into a decision-support framework for brand-equity management.
    • Case Study 4: Xiaomi launch strategy (conjoint). Using conjoint analysis with First-Choice and Logit market simulations, I measured customer preferences, price sensitivity, and feature trade-offs across phone configurations. Stereo sound was the strongest single upgrade, and I separated the configurations that maximize revenue from those that maximize profit.
  • Tools
    Rtidyverseggplot2psychlavaansemPlotGPArotationConjoint analysis
  • What I learned
    • Matching the right method (experience curves, factor analysis, SEM, conjoint) to each business question.
    • Turning statistical output into clear, defensible recommendations a manager can act on.
View code on GitHub ↗View presentations ↗
Analytics ConsultingSpring 2026

Three applied consulting cases, each ending in a practical recommendation.

  • Case studies
    • Case Study 1: Website engagement. Analyzed 50,000 sessions to surface visitor segments, early drop-off, and likely bot traffic, with recommendations to improve retention.
    • Case Study 2: Pain-relief medication. Dose-response modeling and ED50 analysis of a morphine and marijuana combination to assess possible dose reductions.
    • Case Study 3: Credit risk. Compared GLM, sparse GLM, and Random Forest to find key risk drivers and build an interpretable approval rule.
  • Tools
    GLMRandom ForestDose-response modelingSegmentation
  • What I learned
    • Working a vague business question into a concrete analysis and recommendation.
    • Balancing predictive power with interpretability when the decision matters.
View presentations & reports ↗
Prescriptive AnalyticsSpring 2026

A scenario-based LP model setting weekly production of croissants and sandwiches across three Geneva outlets.

  • GoalBalance expected profit, food waste, and shortages under low, base, and high demand, within outlet capacity and service-level limits.
  • Tools
    Linear programmingOptimizationScenario analysis
  • What I learned
    • Modeling real operational decisions under uncertainty.
    • Running sensitivity analyses (oven-capacity loss, demand shifts between locations) to stress-test the plan.
View report ↗
Technologies and Architectures for DataSpring 2026

Designed a data architecture for a supermarket information system covering procurement, inventory, and sales.

  • GoalSupport operational and analytical decision-making with a clean, well-modeled data foundation.
  • Tools
    DFDSequence diagramsERDData engineering
  • What I learned
    • Mapping business processes into DFDs, sequence diagrams, and an ERD.
    • Defining KPIs for supplier reliability, store efficiency, and customer lifetime value (CLV).
View report & slides ↗
Machine LearningTeam of 3Fall 2025

Forecasting Bern’s temperature from meteorological data across 10 Swiss weather stations (Prof. Sebastian Engelke, GSEM).

  • GoalCompare models of different complexity across three forecast horizons and pick the best.
  • Tools
    PythonXGBoostRandom ForestGradient BoostingNeural network
    Best model: XGBoost across all horizons (Kaggle MAE ≈ 1.77 °C).
  • What I learned
    • Feature engineering and careful preprocessing on messy weather data.
    • Non-linear models capture this problem best, and how to compare them fairly across horizons.
View code on GitHub ↗View report ↗
Bachelor’s ThesisSoloSpring & Summer 2025

I forecast demand for SP5-grade steel bloom and billet ingots (150×150 mm) traded on the commodity exchange, comparing models at two levels: the aggregated market and individual producers.

  • GoalFind which forecasting models give the most reliable demand predictions from historical transaction and supply data.
  • Tools
    PythonRegressionLassoRandom ForestXGBoostARIMAX
    Evaluated with RMSE, MAE, MSE, R², MAPE, and MASE.
  • What I learned
    • Turning a messy, confidential real-world dataset into a clean forecasting problem.
    • Tree-based models performed better on aggregated market data, while classical statistical models were more reliable for individual firms with limited history.
View code on GitHub ↗View slides & files ↗
Course: Data & Information AnalysisFall 2024
  • GoalPredict heart-attack risk with classification models, and uncover natural groups through clustering.
  • Tools
    PythonDecision TreeKNNSVMK-meansDBSCAN
  • What I learned
    • Comparing several classification models on the same task.
    • Using clustering to reveal structure in unlabeled data.
Course: MISSpring 2024

A three-part project: I modeled the process in Visual Paradigm, built the database and relationships in SQL with a full dataset and queries, then created Power BI dashboards with DAX.

  • GoalTurn raw operational data into clear, visual answers to real business questions.
  • Tools
    Visual ParadigmSQLPower BIDAX
  • What I learned
    • Building the full path from process model to database to BI dashboard.
    • Writing SQL to answer questions like the year’s best-selling product or the top shipper to branches.
Course: Payment SystemsTeam of 4Spring 2024

Over three phases, my team built a full hiring-and-evaluation framework for a Business Analyst role.

  • GoalDefine how to recruit, evaluate, and onboard for a role from end to end.
  • Tools
    Job analysisInterviewingKPI designOnboarding
  • What I learned
    • Translating a role into measurable KPIs.
    • The full cycle from job design to onboarding, across a four-person team.
Course: Entrepreneurship & Business PlanningTeam of 4Spring 2024

My team shaped the idea and, over three phases, wrote the business plan, business case, and feasibility study, then pitched it.

  • GoalTest whether the idea could stand up as a real business.
  • Tools
    Business planFeasibility studyMarket analysisPricing
  • What I learned
    • Taking an idea from concept to feasibility.
    • Market and competitor analysis, cost-opportunity thinking, and pricing.
Course: Corporate FinanceTeam of 2Spring 2024

With a partner, we picked a listed stock, researched the company, wrote a report, then applied the course’s valuation calculations to it.

  • GoalProduce a grounded valuation and assessment of a real listed company.
  • Tools
    Financial analysisValuation
  • What I learned
    • Reading a company’s financials and value chain.
    • Applying valuation methods to a real stock.
Course: Statistical Quality ControlSpring 2024
  • GoalFind where the model could run more efficiently.
  • Tools
    Statistical quality control
  • What I learned
    • Using SQC techniques to spot inefficiencies in a process.
Course: Systems AnalysisFall 2023
  • GoalPlan the center’s operations grounded in a real needs assessment.
  • Tools
    Systems analysisNeeds assessment
  • What I learned
    • Running a needs assessment and turning it into an operational plan.

Expertise

The tools I work with, and the training behind them.

Skills

Tools

PythonRSQLPower BIExcelVisual ParadigmPowerPointWord

Languages

PersianNativeEnglishProfessional working

Soft skills

CommunicationCreative thinkingTeamworkAnalytical skillsNegotiationConflict resolutionCritical thinking

Certificates & training

2025

Data Analyst

Tose’e Higher Education Institute
2024 – 2025

Data Science course (105h)

Tose’e Higher Education Institute
2023

The 20th National Student Conference on Industrial Engineering

2022

Marketing Management: fundamentals to data-driven (52h)

Tose’e Higher Education Institute
2022

Python Programming Language

2021

Professional Coaching Training course

Dr. Anari, accredited by the ICF
2021

Excel Training course (basic to professional)

Highlights

Recognition, and the things I give my time to.

Honors & awards

2025

🏆First place, Best Intern Competition

2021

⭐Top 0.1% among 127,000 students in the national university entrance exam (Konkur)

2017

🥇First place (national), 5th DADRES competition (Red Crescent rescue-skills olympiad)

2017

📐First place (national), City of Mathematics competition, Ferdowsi University of Mashhad

2016

🏅Diploma of Honor, World Mathematics Invitational Finals (WMI)

Volunteering

2023 – 2024

Member, Scientific Association of Industrial Engineering Students

Amirkabir University of Technology · workshops, conferences, and courses
2023 – 2024

Member, Entrepreneurship Club

Amirkabir University of Technology
2017 – 2021

Member, Red Crescent Youth Organization

2018 – 2019

Student Council Member

ZG

Get in touch

I’m always glad to connect, whether about analytics, collaboration, or coaching. Pick whichever works best for you.

Personal emailUniversity emailLinkedInGitHub