> aadam gafar - data scientist
github ↗ linkedin ↗

Building tools for people who hate wasting time.

about

Software developer transitioning to data science with 3+ years of experience and advanced ML training from Cambridge. Built production-ready forecasting models (Weight Forecasting for Athletes Using Nutrition Data) using SARIMAX, XGBoost, and rigorous statistical validation. Also created Mono, a minimalist YouTube extension with 1000+ users. Strong mathematical foundations (BSc Physics) combined with clean code principles.

Currently open to data science, data engineering, and ML engineering roles. Get in touch at aadamhgafar@gmail.com or via LinkedIn ↗.

projects

education

Career Accelerator in Data Science w/ ML & AI

University of Cambridge ICE · 2025 - 2026
  • Advanced curriculum covering statistical modelling, supervised and unsupervised ML pipelines, deep learning, and Python-based model development and evaluation.
  • Coursework emphasis on rigorous methodology, cross-validation, and real-world deployment - directly informing the forecasting framework project.

BSc Physics

University of Glasgow · 2018 - 2021
  • Applied advanced calculus, linear algebra, probability theory, and statistical inference to complex physical datasets.
  • Developed strong mathematical foundations in error analysis, computational modelling, and scientific reasoning that underpin current data science practice.

experience

Software Developer

Totalmobile Ltd · 2023 - Present
  • Optimised backend data processing and SQL logic for large-scale datasets, improving query throughput and pipeline reliability in a production environment.
  • Engineered automated data validation protocols for RESTful APIs ensuring high data integrity across downstream consumers.
  • Collaborated in an agile engineering team, contributing to code reviews, sprint planning, and production deployments.

Software Developer

Culina Group · 2022 - 2023
  • Designed and deployed ETL pipelines in T-SQL to migrate and normalise logistics data across platforms, improving data quality and accessibility for analytical use.

Data & Analytics Intern

Capita plc · 2022
  • Developed foundational data engineering pipelines and applied machine learning techniques in Python to solve business-centric data problems across large enterprise datasets.

skills

languages & libraries

Python (pandas, NumPy, scikit-learn, statsmodels, XGBoost, Plotly, ruptures), SQL (T-SQL, PostgreSQL)

machine learning & statistics

Time Series Forecasting (SARIMA, SARIMAX), Walk-Forward Cross-Validation, Changepoint Detection, Model Comparison & Selection, Hypothesis Testing (Binomial Test), Correlation Analysis, Multicollinearity Detection, Hyperparameter Tuning (Grid Search)

mathematical foundations

Linear Algebra, Statistical Inference, Probability Theory

data engineering

ETL Pipeline Design, RESTful API Integration, Data Validation, Production ML Constraints

tools & platforms

Git, Jupyter, Google Colab, VS Code, GitHub

product development

CSS Architecture, Browser Extension Development, UI/UX Design

contact

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