DILARA CETINKAYA

Data Scientist

CONTACT

PROFESSIONAL EXPERIENCE

VOLUNTEER

EXPERIENCES

EDUCATION

Data Scientist familiar with the gathering, cleaning, and organizing data for use by technical and non-technical personnel. Advanced understanding of statistical, algebraic, and other analytical techniques. Passionate about working with youth learners. Strong commitment to enhancing and supporting education. Outstanding communication and problem-solving skills.

Dean's List 2020 Fall and 2021 Spring

Relevant Coursework: Calculus I &

Calculus II, Computer Science

Current to May 2024

Associate of Science: Computer Science

Northern Virginia Community

College - Annandale, VA

Completed rigorous data science training covering

python programming, data analysis, data visualization, statistics, regression models, machine learning models, AWS, SQL, and model deployment.

Working as an intern delivering data science ML

Explainibility and Mortgage Modeling projects to clients in the finance industry.

Deci Tech

DATA SCIENTIST INTERN 02/2022 to Current

Fairfax, VA 22033

P: +1-571-***-****

adwbea@r.postjobfree.com

Youth Mentor Volunteer Core

Educational Services –

Chantilly, VA

https://www.linkedin.com/in/munise-

dilara-nur-cetinkaya/

https://github.com/DelilahCetinkaya

Tutor Volunteer, Sylvan

Learning Center – Sterling, VA

09/2020 to 08/2021

12/2017 to Current

ADDITIONAL

Languages: Fluent in Turkish,

English

Certifications & Training:

Certificate Degree in Data

Science (Data Science Vista)

TECHNICAL SKILLS

Data Science: Predictive Modeling, Data Visualization, ML Explainability, and Insight Generation

Programming: Python, SQL, Bash Shell Scripting,

PostgreSQL, BigTable, MongoDB

Machine Learning & Data Analytics Tools: Keras Tensorflow, Pytorch, Scikit-learn, Pandas, Numpy, SciPy, Statsmodels, Google Colab, Anaconda

ML OPS: CI/CD Tools: AWS, Github Actions, Docker,

Kubernetes, Flask, FastApi, HerOKU.

SELECTED PROJECTS

Tested the conceptual soundness of the ML model

explainability framework developed for Fair Lending reporting for financial institutions with a Mortgage Default model that was built using XGB.

Examined SHAP, ALE, ICE/PDP, Total Gain, Average Gain metrics, and Friedman H-Statistics.

Oct 2022

Downloaded and sampled 15 million rows of mortgage performance data from Freddie Mac using AWS S3, AWS Athena, and SQL.

Developed XGB model to predict mortgage defaults with special considerations to class imbalance. The final model had an F1 Score of 70%.

Aug 2022

Designed and implemented a software system to

investigate the role of social media in instilling anti- American sentiment among US allies through

misinformation and disinformation efforts. The project encompassed data collection, data handling, machine learning, and insight generation.

SENTIMENT ANALYSIS Jul 2022

MORTGAGE DEFAULT MODELING

ML EXPLAINABILITY FRAMEWORK

Contact this candidate