Gizem Öge

Logo

2x Datathon Winner | Data Analytics Professional | AI/ML Problem Solver | Driving Insights with Data

View My GitHub Profile

Istanbul/Turkey, Email, LinkedIn Profile, Kaggle Profile

Career Objective

Grounded in a strong foundation of economics, finance, statistics, and academic research, with a focus on solving complex challenges and delivering impactful business solutions. Passionate about leveraging data and AI to uncover actionable insights, optimize processes, and drive strategic decision-making.

Currently participating in the Women in Data Science (WiDS) Datathon 2025.

Technical Knowledge: Python, MS SQL, Stata, Power BI, MS Office, Git & GitHub, Machine Learning Modeling, Time Series Forecasting, Econometric Modeling, Academic Research, Quantitative Analysis, Statistical Inference

Skills: Teamwork, Adaptability, Problem-Solving, Project Management, Decision Making, Analytical Thinking, Time Management, Attention to Detail

Languages: English (Advanced)

Education

Honors & Awards

🥉Third Place Winner — AI for Life Sciences Virtual Hackathon provided by University of Vienna & Gradient Zero (November 2024)

🥇First Place Winner — Women in Datathon provided by UP School & Bitexen (April 2024)

Projects & Certificates

WiDS Datathon 2025

Provided By: Women in Data Science (WiDS), Cornell University, UC Santa Barbara, Ann S. Bowers Women’s Brain Health Initiative (WBHI), Child Mind Institute (Healthy Brain Network), Reproducible Brain Charts (RBC)
Achievements: Ongoing participation
Position: Data Scientist
Duration: January 2025 – Present

About Project: An international team is working to solve a challenge supporting the neurodivergent community. The task involves analyzing demographic, diagnostic, and functional MRI data to build a predictive model for diagnosing ADHD. The aim is to uncover brain regions associated with ADHD in relation to gender roles, which could contribute to improving personalized approaches to treatment and care.



AI for Life Sciences Virtual Hackathon

Provided By: University of Vienna, Gradient Zero, Daiki, Exoscale, Manna Laaz, E-duProof
Achievements: Placed 3rd
Position: Data Scientist
Duration: June 2024 – October 2024

About Project: Freshwater is undoubtedly one of the most vital elements necessary for sustaining life, if not the most crucial. The aim of the project in this virtual hackathon, a challenge set by the University of Vienna, is to forecast groundwater levels and investigate the exogenous variables that significantly affect water level fluctuations. Throughout this challenge, machine learning techniques were optimized, and time series analysis was conducted through 2 tasks.

💧Task 1: Forecasted groundwater levels in Austria over a 2-year period using data from 487 measurement stations, thousands of CSV files, and 11 variables categorized under groundwater, precipitation, water source, and surface water, achieving a 0.15 SMAPE score. Source code can be found here.

💧Task 2: Utilized NASA’s GRACE data to extract groundwater information through data mining and conducted a 5-year forecast, testing the impact of exogenous variables. Source code can be found here.


Click on the image to watch the presentation video.



Women in Datathon

Provided By: UP School, Bitexen
Achievements: Placed 1st
Position: Data Scientist
Duration: April 2024

About Project: A machine learning prediction model was constructed to observe the impact of health status, labor force participation, gender roles, and political representation on wage inequality in the context of gender roles. Additionally, the influence of gender roles on job placement was investigated. The competition was hosted on Kaggle, and Python and Markdown Language were used to complete the project. Here is the Kaggle Notebook.


Click on the image to watch the presentation video.



Churninator 🤖

Provided By: Miuul Data Scientist Bootcamp Final Project, Istanbul Kodluyor Project
Achievements: Placed 2nd
Position: Data Scientist
Duration: February 2024 - May 2024

About Project: Istanbul Kodluyor is Turkey’s first social impact bond, coordinated by the Ministry of Industry and Technology and implemented by the Istanbul Development Agency (İSTKA), with investment from Bridges Outcomes Partnerships. The training was provided by Enocta and managed by Tobeto. After completing preparatory courses in programming, software development, and data science with Enocta, participants gained membership in the Istanbul Kodluyor Project and access to Miuul’s Data Scientist Bootcamp.

The bootcamp covered topics such as feature engineering, MS SQL, machine learning, and Python, including over five real-world projects. The final project Churninator, involved a team effort to develop a machine learning product predicting customer churn in a bank, achieving a 92% success rate. A Streamlit web app was created to showcase the product, featuring an interactive dashboard and using RFM analysis for customer segmentation. Source code can be found here.