Skip to content
View charlesakinnurun's full-sized avatar
7 followers · 37 following

Achievements

Achievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

Achievements

Achievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

Block or report charlesakinnurun

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
charlesakinnurun/README.md

Hi there, I'm Charles 👋

Website Gmail LinkedIn

Applied Data Scientist and AI/ML Engineer focused on building production-ready machine learning systems. Experienced in developing and deploying models across NLP, computer vision, and generative AI, with a strong emphasis on scalability and real-world impact. Proficient in PyTorch, Scikit-learn, and AWS, with hands-on experience in MLOps, predictive modeling, and statistical analysis to drive data-informed decision-making.

Over the past four years, I have developed strong programming expertise across both imperative and declarative paradigms, working extensively with Python, R, and SQL to solve data-driven problems. My experience spans multiple industries including technology, finance, consulting, aviation, and healthcare where I have applied data science, statistical analysis, and machine learning techniques to real-world use cases.

Currently studying Computer Science and an active member of the National Association of Computing Students.

I am passionate about designing, training, and deploying intelligent AI and machine learning solutions that deliver meaningful business impact.

📦 Projects 📜 Language ⭐ Stars 🔀 Forks ℹ️ Issues 📬 Pull requests
British Airways Python Stars Forks Issues Pull Requests
Lloyds Banking Group Python Stars Forks Issues Pull Requests
BCG X OCaml Stars Forks Issues Pull Requests
JP Morgan Chase OCaml Stars Forks Issues Pull Requests
Citi OCaml Stars Forks Issues Pull Requests
BCG X OCaml Stars Forks Issues Pull Requests
Quantium OCaml Stars Forks Issues Pull Requests
Deloitte Tableau Stars Forks Issues Pull Requests

Technical Skills

Python R SQL Julia Scikit-learn Pandas NumPy SciPy Matplotlib Seaborn PyTorch TensorFlow Keras NLTK spaCy OpenCV OpenNLP LightGBM XGBoost Beautiful Soup MySQL SQLite Docker Kubernetes AWS Git Github Actions Jupyter Notebook Streamlit Power BI Excel LangChain Langraph Hugging Face Pytest JSON

Pinned Loading

  1. british-airways-data-science british-airways-data-science Public

    🔥 Engineered a machine learning pipeline achieving 85% accuracy in predicting customer booking behavior, and a scalable demand forecasting framework supporting capacity planning across 1,500+ daily…

    Python

  2. lloyds-bank-data-science-and-analytics lloyds-bank-data-science-and-analytics Public

    ▪ Partitioned 1,000 records into an 80/20 stratified train/test split (800 train, 200 test), then applied SMOTE to resolve a 796:204 class imbalance in the training set.

    Python

  3. BCGX-data-science BCGX-data-science Public

    ▪ Engineered 12 price-differential features across off-peak, peak, and mid-peak tariff periods (December vs. January deltas + cross-period averages) to capture seasonal pricing dynamics as churn si…

    Jupyter Notebook

  4. BCGX-generative-AI BCGX-generative-AI Public

    ▪ Built a rule-based financial chatbot in Python powered by 2 CSV datasets (final_data_report.csv, Summary_final_report.csv), covering 3 companies (Apple, Microsoft, Tesla) across 3 fiscal years (2…

    Python

  5. jpmc-quantitative-research jpmc-quantitative-research Public

    Built a natural gas price forecasting model (Task 1) on 48 months of historical data (Oct 2020 – Sep 2024) using a SARIMAX(1,1,1)×(1,1,1,12) model to capture 12-month seasonal cycles, then extended…

    Jupyter Notebook

  6. citi-mqa citi-mqa Public

    ▪ Built a multi-model quantitative pricing framework for coffee futures options, applying 3 distinct financial models: Cost of Carry, Black-Scholes, and Monte Carlo Simulation to derive the fair va…

    Jupyter Notebook