Baran Nama

NLP ML Engineer

Geneva, Switzerland

+41 77 931 10 39

I am an engineer looking for a data scientist or ML Engineer position. I am experienced at building regression and forecasting models, conducting time-series analysis, developing data-driven NLP applications, and training deep and shallow learning models at scale.

Education

École polytechnique fédérale de Lausanne (EPFL)

Computer Science, Machine Learning • 2017 - 2019

Computer Science learning, Machine learning, Statistics, Applied DS, Data visualization.

Istanbul Technical University (ITU)

Computer Science, Computer Engineering • 2012 - 2017

OOP Programming, Computer, and Software Engineering.

Work Experience

NLP ML Engineer

World Intellectual Property Organisation • July 2022 - Present

- Working and improving the end-to-end Neural Machine Translation pipeline of WIPO.
- Working on different multilingual ML problems such as text classification.

Data Scientist

DataZoo • March 2021 - Present

- Creating, maintaining, and improving data and forecasting model pipelines using third-party tools on various cloud platforms (AWS).
- Implementing new statistical and forecasting methods on NumPy and optimizing them using Numba.
- Working on different forecasting projects like sale and demand forecasting, resource allocation from different customers.
- Containerization of predictive machine learning models and delivery of them to the customers.
- Software development and main forecasting product improvements.

Data Scientist

Logitech • August 2018 - September 2019

- Implementation of a demo file analyzer using Golang.
- 1.5 million data points were collected by scraping several matchmaking servers using a self-implemented distributed web crawler created via Python (BS4) on the AWS (EC2, S3) platform.
- Data labeling by aggregating data points using the TrueSkill algorithm in Python.
- Preliminary exploratory data analysis, feature augmentation, and selection using Matplotlib, Seaborn, Plotly, Pandas, Pyspark, and Sklearn.
- EDA and multivariate time series analysis of player rankings using PySpark and MLlib.
- Future player ranking forecasting using shallow learning models (linear models, SVM, Random Forest, Boosting Trees like XGBoost, LightGBM) and deep learning models implemented with Keras and Tensorflow.
- Acquired 0.9 R2 score using XGBoost on average by backtesting time series.

Skills

Python PySpark C/C++ Golang Tensorflow/Keras PyTorch SQL Tableau Web Scraping AWS Flask-RESTful Time-series forecasting Deep Learning Natural Language Processing Machine Learning Jupyter Notebooks Pandas Sklearn Matplotlib NumPy Azure Bash/Linux Huggingface Data Visualisation

Languages

English — Professional Turkish — Native or Bilingual