About Me

Hi! My name is Israel Igietsemhe, and I am a seasoned data scientist with over seven years of experience. I am passionate about applying machine learning models to solve complex business problems. I have worked on a range of projects focused on demand forecasting, inventory optimization, health care analytics, finance, recommender systems and new product introduction. What I love most about building machine learning models is the ability to turn complex data into actionable insights that drive meaningful change. Whether I am working on a forecasting model or an optimization algorithm, I am always driven by the challenge of finding new ways to use data to solve real-world problems. I am currently working as a Lead Data Scientist at KetteQ, a SaaS company in supply chain optimization, where I continue to explore new opportunities to apply my skills and expertise.

Python
Programming

Machine
Learning

Data Analytics

Research

I love working in exciting teams solving challenging problems!

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Skills

I am equipped with diverse skills by the virtue of my hard work. In the past, I have worked on multiple projects in churn prediction, natural language processing, demand forecasting and supply chain optimization. Explore my website to get more details about my experience.


Few of my skills are

Machine Learning

99%

Python

99%

R

99%

Docker/Flask/Model deployment

90%

SQL

95%

Git/GitHub/GitLab/Bitbucket

95%

Google Cloud Platform/Big Query ML

90%

Pyspark

80%

Natural Language Processing/GPT

95%

Microsft Azure

80%

Work Experience

KetteQ Sep 2022 - Present

Lead Data Scientist

  • Responsible for the development and deployment of machine learning forecasting models on the KetteQ demand and operations planning software platform.

    • Afrilearn May 2022 - Present

    Machine Learning Consultant

  • Participating in a $100,000 UNICEF-sponsored project for making education more accessible to indigent students across Africa. Deploying a recommender system for an e-learning platform using an association rule mining model. Applying Open AI ChatGPT for generating multiple choice questions for quiz games.

    • Loblaw Companies Limited Sep 2021 - April 2022

    Data Science Analyst, Healthcare Data Products and Services

  • Implemented a boosted tree classifier on Big Query ML for predicting patient churn with an average accuracy of 65%.
  • Initiated and implemented a patient likelihood classification model for predicting the most likely patients to respond to a recommendation notification with about 70% testing accuracy.
  • Researched and presented concepts on the mathematics of deep learning and recommender systems to my team members on a biweekly basis.

    • Sharpest Minds April 2021 - Aug 2021

    Data Science Fellow

  • Built a full-stack machine learning web application that classifies Amazon product reviews using natural language processing. The product helps customers clarify product selection based on keywords discussed in product reviews.
  • Created an original dataset by building a data pipeline that scrapes Amazon product reviews using beautiful soup and performs data cleaning to prepare the dataset for topic modeling.
  • Applied natural language processing topic modeling using a latent dirichlet allocation model in scikit-learn to identify keywords for each product review.
  • Deployed model as a web-app using Python Flask and Heroku.

    • University of Toronto Sep 2016 - Aug 2021

    Machine Learning Team Lead, Dynamic Optimization and Operations Management LAB

  • Collected user and business requirements and put together detailed project plans.
  • Developed random forest models in helping Nestle Canada in underpayment claims classification.
  • Developed a local heuristic search model for inventory allocation for Nestle Canada with potential reduction in penalty costs up to $200,000 monthly.
  • Developed demand forecasting models using multivariate regression models, generative adversarial networks and Bayesian LSTMs using keras, sk-learn, Tensorflow and PyTorch.

    • CIBC Jul 2017-Aug 2017

    Machine Learning Specialist

  • Initiated an unsupervised learning system for anomaly detections in managing role-based access control using hierarchical clustering of the jaccard distances.
  • Implemented python codes using selenium package for web scraping of employee profiles.

    • Contact

    Thank you for going through my portfolio, don't hesitate to send me an email at aloagbaye.i@gmail.com

    If you would like to work with me, feel free to connect with me.