Merve Acar, Developer in Santa Clara, CA, United States
Merve is available for hire
Hire Merve

Merve Acar

Verified Expert  in Engineering

Data Scientist and Software Developer

Location
Santa Clara, CA, United States
Toptal Member Since
March 11, 2019

Merve is an experienced machine learning engineer who takes pleasure in revealing the story of data and building predictive models with a proven track record of designing and implementing pipelines for extracting, validating, cleaning, transforming, and modeling data. She is passionate about solving real-world industry problems and eager to take on new challenges and opportunities.

Portfolio

Trust & Safety Laboratory
Python, SQL, Amazon Web Services (AWS), Amazon EC2, Amazon S3 (AWS S3)...
Turkish Aerospace Industries
Computer Vision, Data Mining, Data Science, Deep Learning, Git, Jira, Python 3...
Vitus Commodities
Slack, Jira, Git, Plotly, Selenium, RapidMiner, Keras, PyTorch...

Experience

Availability

Part-time

Preferred Environment

Git, PyCharm, Jupyter Notebook, Linux, Windows, Amazon EC2, Jira, Slack

The most amazing...

...automated machine learning tool I've developed leverages the meta-learning power to select the most optimal algorithm and its parameters, adapting to any task.

Work Experience

Data Analyst

2023 - PRESENT
Trust & Safety Laboratory
  • Automated the collection of social media accounts spreading misinformation from multiple fact-check sites.
  • Utilized Hugging Face's CLIP model for zero-shot learning to detect harmful content in images.
  • Performed analyses to visualize bad actors and their friends of friends network graphs.
  • Translated client requirements into interactive dashboards in Tableau.
Technologies: Python, SQL, Amazon Web Services (AWS), Amazon EC2, Amazon S3 (AWS S3), Protobuf, Okta, Twitter API, Beautiful Soup, NetworkX, Gephi, Snowflake, OpenAI GPT-3 API, Tableau, Bazel, Databases, Data Cleaning, Data Analytics, Social Media

Data Scientist

2019 - 2022
Turkish Aerospace Industries
  • Developed a dashboard-based surveillance system to improve a factory's work processes using IP camera recordings. Applied video and image processing algorithms using the OpenCV library together with object detection and object tracking algorithms.
  • Built an LSTM-based model to identify people's actions and improve work processes in a factory.
  • Developed a predictive maintenance model using ARIMA and LSTM algorithms which provides insight into a plane part's breakdown using the time-series data of a plane component. Applied data manipulation, analysis, and visualization.
Technologies: Computer Vision, Data Mining, Data Science, Deep Learning, Git, Jira, Python 3, Object Detection, Object Tracking, Time Series Analysis, MySQL, PostgreSQL, PyTorch, Long Short-term Memory (LSTM), Bash Script, Keras, PyCharm, Windows, Jupyter Notebook, Neural Networks, Scikit-learn, Matplotlib, Visualization, Seaborn, SQL, Data Visualization, Software Engineering, Convolutional Neural Networks (CNN), Data Analysis, Machine Learning, Python, Pandas, Data Modeling, Data Processing, Supervised Machine Learning, Regression, Classification, CSV, Reports, Data Scientist, OpenCV, You Only Look Once (YOLO), Data Cleaning, Data Analytics, Artificial Intelligence (AI)

Machine Learning Engineer

2016 - 2019
Vitus Commodities
  • Took part in several data scraping projects using Selenium, API calls, the requests library, and more.
  • Created reports on Microsoft Power BI for data visualization.
  • Implemented a multilayer perceptron model using Python and Keras to forecast the natural gas demand in the UK for the coming days.
  • Deployed an LSTM model that predicts Turkey's electricity price for the next few days.
  • Implemented a scraper to obtain and manipulate GFS weather data to use as a source for model training.
  • Investigated deep learning methods to enhance the performances of the current working models for time-series data.
  • Implemented an outlier detection project consisting of probabilistic and clustering-based algorithms and an autoencoder method to detect extreme days concerning the UK's natural gas demand.
Technologies: Slack, Jira, Git, Plotly, Selenium, RapidMiner, Keras, PyTorch, Microsoft Power BI, MySQL, Python, Amazon Web Services (AWS), Amazon S3 (AWS S3), Amazon EC2, Long Short-term Memory (LSTM), XGBoost, Data Visualization, Data Scraping, Time Series Analysis, PyCharm, Windows, Neural Networks, Scikit-learn, Matplotlib, Slack API, Seaborn, SQL, Amazon RDS, Data Mining, Convolutional Neural Networks (CNN), Data Analysis, Machine Learning, Predictive Modeling, Pandas, Data Modeling, Data Processing, Web Scraping, Supervised Machine Learning, Regression, Classification, Decision Trees, APIs, Statistical Analysis, Exploratory Data Analysis, Databases, Data Cleaning, Data Analytics, Artificial Intelligence (AI)

Machine Learning Engineer

2016 - 2017
Independent Work
  • Implemented data preprocessing, data imputation, feature extraction, and model creation modules for the Vitriol project using Scala.
  • Researched and tested a meta-learning strategy to predict the best model with the best parameters for a given problem using Scala and Spark.
  • Implemented a parser to handle unstructured data that comes from different sources using Python.
  • Worked with big data using Apache Spark framework and Scala.
Technologies: Git, PostgreSQL, Spark, Scala, Python, Python 3, Data Science, Spark ML, Matplotlib, Visualization, Seaborn, Data Mining, Data Analysis, Machine Learning, Predictive Modeling, Data Modeling, Data Processing, Supervised Machine Learning, Regression, Classification, Decision Trees, Decision Modeling, Data Cleaning

Software Developer

2015 - 2015
C3S Command Control & Cybernetic Systems
  • Developed connector reliability testing software that controls the connection between PCI cards and connectors on the Linux platform.
  • Built software that calculates how much time an employee spends at the office.
  • Wrote SQL database queries to analyze an employee's working schedule.
Technologies: MySQL, Python, C++, Linux, PostgreSQL, SQL

Software Test Developer

2014 - 2014
Taleworlds Entertainment
  • Developed automated tests for Mount&Blade: Bannerlord II project.
  • Monitored test results and reported bugs found in prerelease software on a daily basis.
  • Performed unit tests and integration tests to determine if the game scenes were working correctly.
  • Worked within an Agile environment with multiple teams.
Technologies: Git, C++

Software Developer

2014 - 2014
TUBITAK | The Scientific and Technological Research Council of Turkey
  • Developed a parental control tool for Pardus, a Linux distribution supported by the Turkish government.
  • Implemented content filter, usage control, and monitoring modules.
  • Gained experience in open-source development and the security field.
Technologies: PyQt, Bash, Python, Linux, Bash Script

Vitriol

http://senior.ceng.metu.edu.tr/2016/mallorn/
This is an automated machine learning tool that uses machine learning and data mining techniques for preprocessing data and choosing the machine learning model automatedly for a given problem.

I used a meta-learning strategy to select the most appropriate algorithm and its parameters. This project is implemented in Spark and the Scala programming language to handle big data.

Natural Gas Demand Forecasting

This project aims to predict the UK's gas demand using several techniques, such as feature engineering and data augmentation.

First, I implemented an extreme day detection module to label the data as extreme or not extreme. An oversampling method helped enhance extreme days because they were a small portion of the data. I also implemented a dynamic weighted ensemble model using a multilayer perceptron (MLP) and a linear regression model to consider both linear and non-linear trends.

Stock Price Prediction

The goal of the project is to predict stock prices over the Frankfurt Stock Exchange, including those for BMW and Daimler. I built RNN, GRU, and LSTM models in PyTorch because it is a time series problem.

Denoising of Images

In this project, I mainly implemented various generative networks, and their components to perform unsupervised learning for the generation of new data samples (images) and, the denoising of images.

PriceTag

This project aims to predict the market prices of products in several domains using pictures and their corresponding market values. I trained a convolutional neural network to predict the price of a given product using Python and Keras.

Pardus Gozcu

This is a parental control tool consisting of content filtering, usage time controlling, usage management (for allowing/blocking a set of software types), and monitoring to watch and report user activities. It is an open-source project developed for Pardus, a Linux distribution, using PyQt, Python, and Bash.
2017 - 2020

Master's Degree in Computer Engineering

Istanbul Technical University - Istanbul, Turkey

2012 - 2016

Bachelor's Degree in Computer Engineering

Middle East Technical University - Ankara, Turkey

FEBRUARY 2023 - PRESENT

Using Python to Access Web Data

Coursera

OCTOBER 2022 - PRESENT

Structuring Machine Learning Projects

Coursera

SEPTEMBER 2022 - PRESENT

Convolutional Neural Networks

Coursera

SEPTEMBER 2019 - PRESENT

Practical Time Series Analysis

Coursera

SEPTEMBER 2019 - PRESENT

Fundamentals of Visualization with Tableau

Coursera

AUGUST 2019 - PRESENT

Google Cloud Platform Big Data and Machine Learning Fundamentals

Coursera

SEPTEMBER 2018 - PRESENT

Neural Networks and Deep Learning

Coursera

NOVEMBER 2016 - PRESENT

Machine Learning Foundations: A Case Study Approach

Coursera

Libraries/APIs

Matplotlib, Scikit-learn, Pandas, PyTorch, Keras, Slack API, XGBoost, OpenCV, Natural Language Toolkit (NLTK), Spark ML, PyQt, Beautiful Soup, TensorFlow, Protobuf, Twitter API, NetworkX

Tools

Microsoft Power BI, PyCharm, Slack, Git, Seaborn, Plotly, Tableau, Jira, Bazel, You Only Look Once (YOLO)

Languages

Python, SQL, Python 3, Bash, Scala, C++, Haskell, Bash Script, R, XML, Snowflake

Paradigms

Data Science

Industry Expertise

Social Media

Frameworks

Selenium, Spark

Platforms

Windows, Linux, Jupyter Notebook, RapidMiner, Amazon EC2, Amazon Web Services (AWS), Gephi, AWS Lambda

Storage

PostgreSQL, MySQL, Data Pipelines, Databases, Amazon S3 (AWS S3), JSON

Other

Machine Learning, Predictive Modeling, Data Processing, Web Scraping, Google Colaboratory (Colab), Regression, Classification, Decision Trees, Artificial Intelligence (AI), CSV, Exploratory Data Analysis, Data Cleaning, Computer Vision, Metric Learning, Time Series, Data Mining, Visualization, Deep Learning, Statistics, Object Detection, Object Tracking, Time Series Analysis, Data Visualization, Software Engineering, Convolutional Neural Networks (CNN), Image Analysis, Neural Networks, Data Structures, Data Analysis, Data Modeling, Version Control Systems, Models, Modeling, Communication, Data Analytics, APIs, Data, Unsupervised Learning, Supervised Machine Learning, Decision Modeling, Data-driven Decision-making, Data Engineering, Dashboards, Reports, Data Scientist, Statistical Analysis, Remote Sensing, Natural Language Processing (NLP), Cloud Services, Design, Machine Learning Automation, Gated Recurrent Unit (GRU), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM), Data Scraping, Feature Analysis, Amazon RDS, Sentiment Analysis, Generative Pre-trained Transformers (GPT), Okta, OpenAI GPT-3 API

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring