Hi! I'm Amir FARES a Junior Machine Learning Engineer and Data Scientist

Leveraging data for intelligent solutions.

As a recent top-performing graduate holding a Master's degree in Computer Science and AI, I bring a wealth of experience to the table. From my tenure as a Data Analyst at Sonatrach to my roles as an ML Engineer at Wessini and a Lead ML Engineer at Omdena, I've consistently delivered results. My track record includes triumphs in ML competitions and certifications from esteemed industry leaders such as Deeplearning.AI, NVIDIA, and Google. Furthermore, my Master's thesis delves into cutting-edge NLP research. I invite you to explore my portfolio and witness firsthand how I can infuse innovation and expertise into your team's projects.

    Education:
  • (M.S.) Master's degree in Fundamental of computer science and artificial intelligence | Ferhat Abbas University, Setif, Algeria (2021-2023)
  • (B.S.) Bachelor's degree in computer systems | Larbi Tebessi University, Tebessa, Algeria (2018-2021)

Professional Experience

๐Ž๐Œ๐ƒ๐„๐๐€ (Remote, Algeria)

Lead ML Engineer, Data Scientist, Project Manager Assistant

October 2023 โ€“ February 2024

- ๐‹๐ž๐š๐ ๐Œ๐‹ ๐„๐ง๐ ๐ข๐ง๐ž๐ž๐ซ & ๐๐ซ๐จ๐ฃ๐ž๐œ๐ญ ๐Œ๐š๐ง๐š๐ ๐ž๐ซ ๐€๐ฌ๐ฌ๐ข๐ฌ๐ญ๐š๐ง๐ญ: Provided guidance and leadership to a team over a 5-month period, contributing to the successful completion of a project.

- ๐€๐ˆ-๐ƒ๐ซ๐ข๐ฏ๐ž๐ง ๐–๐š๐ญ๐ž๐ซ ๐€๐ฏ๐š๐ข๐ฅ๐š๐›๐ข๐ฅ๐ข๐ญ๐ฒ ๐…๐จ๐ซ๐ž๐œ๐š๐ฌ๐ญ๐ข๐ง๐ : Spearheaded the development of an AI-driven solution for forecasting water availability, from research and data collection to modeling and deployment. Achieved remarkable accuracy and transformed an open-source project into a user-friendly forecasting app capable of predicting water availability for various timeframes, from months to a decade ahead.

- ๐๐ž๐ซ๐ฌ๐จ๐ง๐š๐ฅ ๐š๐ง๐ ๐‚๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐†๐ซ๐จ๐ฐ๐ญ๐ก: Embraced opportunities for personal and professional development, learning from industry leaders and participating in workshops. Played a pivotal role in an open-source project, collaborating with diverse contributors and enhancing communication skills as a lead in machine learning.

๐–๐„๐’๐’๐ˆ๐๐ˆ (Algiers, Algeria)

ML Engineer

November 2023 โ€“ January 2024

- ๐— ๐—ผ๐˜€๐˜ ๐—ผ๐—ณ ๐—บ๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ ๐—ถ๐—ป๐˜ƒ๐—ผ๐—น๐˜ƒ๐—ฒ๐—ฑ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€ ๐˜€๐—ฝ๐—ฒ๐—ฐ๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐—น๐—น๐˜† ๐—ก๐—Ÿ๐—ฃ

๐’๐Ž๐๐€๐“๐‘๐€๐‚๐‡ (GEM, Oued Safsaf, Tebessa, Algeria)

Data Analyst Intern

February 2023 โ€“ March 2023

- ๐ˆ๐ง-๐ƒ๐ž๐ฉ๐ญ๐ก ๐Š๐๐ˆ ๐€๐ง๐š๐ฅ๐ฒ๐ฌ๐ข๐ฌ: Conducted an extensive analysis of SONATRACH's seven key performance indicators (KPIs) related to its 1,644.537 km pipeline network and 45 sectioning stations. This analysis contributed to operational improvements and informed decision-making.

- ๐ƒ๐š๐ญ๐š-๐ƒ๐ซ๐ข๐ฏ๐ž๐ง ๐ƒ๐ž๐œ๐ข๐ฌ๐ข๐จ๐ง ๐„๐ง๐ก๐š๐ง๐œ๐ž๐ฆ๐ž๐ง๐ญ: Discovered critical correlations between KPIs, including the annual transport capacity of 33.2 billion cm^3, and SONATRACH's strategic goals. This data-driven approach significantly improved decision-making processes, aligning business strategies with operational objectives.

- ๐๐ซ๐จ๐Ÿ๐ž๐ฌ๐ฌ๐ข๐จ๐ง๐š๐ฅ ๐†๐ซ๐จ๐ฐ๐ญ๐ก: Through this full-time internship with a rigorous 8-hour daily schedule, I deepened my expertise in CIMIX software, mastering analysis of seven KPIs. My proficiency in Excel also improved significantly. This hands-on experience equips me with concrete skills, setting the stage for a career as a data analyst, where I can leverage my knowledge gained from SONATRACH's extensive infrastructure.

Projects

JUMIA Sentiment Analysis - ML Olympiad

๐Ÿ† Top 3 Finisher ๐Ÿ† Check the button below for more details about the challenge and how to analyze customer sentiments from their reviews.
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ChatGPT Answer Classification Challenge ๐Ÿค–

Ranked top 20 in the ML Olympiad's ChatGPT Answer Classification Challenge, I developed a model enhancing AI-generated content credibility.
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Fashion MNIST CNN Classifier

showcasing our image classification prowess with a 92.3% validation accuracy. Overcoming challenges in discerning similar fashion classes underscores our commitment to robust, accurate solutions.
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Maven Environmental Challenge: Tracking Apple's Carbon Neutrality Journey ๐Ÿ๐ŸŒ

Explore Apple's carbon neutrality journey through the lens of data-driven insights and journalism in my Maven Environmental Challenge project. Uncover the strategic decisions driving sustainability, such as the charger removal in 2020.
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Spaceship Titanic: Predicting Alternate Dimension Transportation ๐Ÿš€

In this Project, I unravel the cosmic mystery of the Kaggle Spaceship Titanic competition. "CryoSleep" played a crucial role in predicting alternate dimension transportation. Achieved an impressive 0.80 score, ranking in the top 28% among 2062 teams, contributing to the rescue mission's success.
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Store Sales Forecasting

Dedicated to optimizing store sales predictions for Corporaciรณn Favorita, our project employs time series forecasting and machine learning. Using XGBoost, achieved a promising score of 1.45784, laying a solid foundation for further refinement and improvement.
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UmojaHack Africa 2023: Carbon Dioxide Prediction Challenge (BEGINNER) ๐ŸŒ๐Ÿ“Š

Discover my journey and accomplishments in the Zindi Challenge, where I leveraged machine learning and satellite data to predict carbon emissions in Africa, aiding vital monitoring efforts across the continent. I achieved a top 50% ranking in this impactful competition.
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Onyx Data DataDNA Challenge - Spotify Most Streamed Songs 2023 Dataset - October 2023 ๐ŸŽง

Explore my data-driven journey through the Spotify Most Streamed Songs 2023 Dataset, where I uncover the secrets behind song popularity on Spotify. Discover insights for the modern music landscape.
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Tunisia Energy Fraud Detection STEG

In the fight against electricity and gas fraud in Tunisia, I secured a top 25% position on the leaderboard, using an XGBoost model with an AUC of 0.86. Analyzing billing history, the solution safeguards STEG's revenues, minimizing losses.
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Certifications

  • Machine Learning Specialization (DeepLearning.AI - coursera)
  • Deep Learning Specialization (DeepLearning.AI - coursera)
  • Fundamentals of Deep Learning (NVIDIA)
  • Accelerating End-to-End Data Science Workflows (NVIDIA)
  • Introduction to Data Analysis Using Excel (RICE UNIVERSITY - coursera)
  • Introduction to Git and GitHub (Google - coursera)