Abdellatif Dalab, Data Scientist and Software Developer in Toronto, ON, Canada
Abdellatif Dalab

Data Scientist and Software Developer in Toronto, ON, Canada

Member since December 8, 2021
Abdellatif is a senior ML engineer with four years of industry experience at Repustate Inc, Decathlon Canada, and Societe General Investment Banking. His strengths include researching, developing, and clearly communicating algorithmic solutions that solve novel business and scientific problems.
Abdellatif is now available for hire


  • Repustate
    Artificial Intelligence (AI), AWS S3, Algorithms, Deep Learning...
  • Decathlon
    Python, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy...
  • Decathlon
    Python, Algorithms, Keras, SQL, TensorFlow, Jenkins Pipeline, GitHub


  • Python 5 years
  • NumPy 4 years
  • Pandas 4 years
  • Scikit-learn 4 years
  • SQL 3 years
  • Natural Language Processing (NLP) 2 years
  • Hugging Face 1 year
  • PyTorch 1 year


Toronto, ON, Canada



Preferred Environment

Google Colaboratory (Colab), Visual Studio Code, Jupyter Notebook, MacOS, GitHub

The most amazing...

...project I've worked on at Repustate is designing and developing a new generation v5 DL microservices API that communicates with a monolith Go-based application.


  • Senior Machine Learning Engineer

    2022 - PRESENT
    • Developed novel and interpretable deep learning solutions for text in PyTorch by implementing and improving multiple research paper algorithms.
    • Implemented quantization strategies using ONNX, reducing model sizes by up to 5x and increasing inference time by up to 3x.
    • Designed and developed a new generation gRPC microservices API, allowing the main application, which is developed in Go, to communicate with Python deep learning servers.
    • Reduced server size by up to 8x compared to the previous generation API and increased inference speed by 2x-3x on average for prediction tasks.
    • Reduced the server size by up to 8x compared to the previous generation API.
    • Designed and developed AWS S3 schemas for production models and tokenizers.
    • Developed custom deep learning Docker images and RPM packages to be installed on on-premise RHEL/Linux servers. Tech used: RPM, AWS EC2, Docker, Git.
    • Orchestrated a continuous labeled-data generation pipeline that extracts labeled text using SQL daily and then stores the data in a designed S3 data lake to be used in future language models.
    • Designed and developed a custom MLflow tracking server to record and monitor experimentation results and artifacts.
    • Managed a multi-client portfolio (Banking, Govt, Healthcare, Marketing, Retail), led technical discussions on sales calls, and aided in landing over five clients.
    Technologies: Artificial Intelligence (AI), AWS S3, Algorithms, Deep Learning, Open Neural Network Exchange (ONNX), Hugging Face, gRPC, Protobuf, Git, Go, Python, PyTorch, Keras, MLflow, Amazon EC2, Docker, NumPy, Scikit-learn, Pandas
  • Data Scientist

    2020 - PRESENT
    • Developed an in-house data-visualization pipeline that replaced a licensed tool saving $60,000 per year. Used SQL, Git, Jenkins, AWS cloud, Google Sheets API, and Google Data Studio.
    • Prototyped an NLU solution with customer reviews classification, keyword extraction, and sentiment analysis that outperformed a licensed tool, saving the marketing team $15,000 per year.
    • Created a visual search engine that was deployed as a product retrieval API. It's currently being used for product recommendations.
    • Built an unsupervised topic modeling solution for customer reviews with visualization, using sentence transformers. Improved original solution using GPT2 and prompt engineering.
    • Developed a store turnover forecasting tool using additive models and custom-made regressors (Prophet API).
    • Engineered an NLU product-article recommendation solution as part of Decathlon's personalization strategy.
    • Worked on data extraction, transformation, and loading tasks for each solution.
    • Made a sustainability reporting tool to monitor the performance of second-life and eco-designed products.
    • Built a color detection solution using k-means clustering to aid internal object detection models.
    • Interviewed new data science candidates, actively contributed to the hiring process, and mentored new interns on various data-related tasks.
    Technologies: Python, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Flask, Google Data Studio, Redshift, AWS S3, Google Cloud Platform (GCP), Jenkins Pipeline, GitHub
  • Machine Learning Intern

    2019 - 2019
    • Developed and deployed a deep recommendation model for user-click item prediction using LSTM RNN architecture.
    • Surpassed the benchmarked precision, recall, and coverage metrics by improving the solution using attention models.
    • Developed and deployed an object detection model using TensorFlow's API for a hockey-brand detection application.
    Technologies: Python, Algorithms, Keras, SQL, TensorFlow, Jenkins Pipeline, GitHub
  • Machine Learning Developer Intern

    2018 - 2018
    Societe Generale
    • Developed a BI reporting tool using MicroStrategy.
    • Contributed to data visualization projects using Tableau.
    • Helped develop a web application using the Django framework.
    Technologies: Tableau, Python, Django, MicroStrategy


  • AI Game Project and Report

    This project was part of the AI course (COMP 472) at Concordia University. It includes a detailed report of the implementation strategy, minimax algorithm, and outcome analysis. The Jupyter Notebook has most of the code. The project didn't utilize any existing libraries. All algorithms and heuristics were implemented from scratch.


  • Languages

    Python, SQL, HTML, CSS, JavaScript, Go
  • Libraries/APIs

    Keras, Scikit-learn, NumPy, Pandas, PyTorch, TensorFlow, React, Protobuf
  • Other

    Artificial Intelligence (AI), Data Analysis, Machine Learning, Natural Language Processing (NLP), Google Data Studio, Deep Learning, OOP Designs, Data Structures, Algorithms, Principles of Programming Languages, Hugging Face, MicroStrategy, Google Colaboratory (Colab), Sci-kit learn, Open Neural Network Exchange (ONNX), MLflow
  • Tools

    Jenkins, GitHub, Tableau, Git
  • Storage

    Redshift, PostgreSQL, AWS S3, MySQL, Google Cloud Datastore, Database Programming
  • Frameworks

    Flask, Django, gRPC
  • Paradigms

    Database Design
  • Platforms

    Google Cloud Platform (GCP), Visual Studio Code, Jupyter Notebook, Docker, MacOS, Amazon EC2


  • Bachelor's Degree in Information Technology
    2015 - 2019
    Concordia University - Montreal, Quebec, Canada


  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
  • Structuring Machine Learning Projects
  • Neural Networks and Deep Learning
  • Machine Learning with Python in Data Science
    JULY 2017 - PRESENT
  • Machine Learning
    APRIL 2017 - PRESENT
    Stanford University | via Coursera

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