Mark McAllister

I'm a

About

Hi there, my name is Mark McAllister. I am a recent graduate with a first-class bachelor's degree in Computing Science and a Master's Degree with distinction in Artificial Intelligence from the University of Stirling. I have a passion for machine learning and data science, and I am eager to apply my skills and knowledge to real-world problems.

Data Science and Software Development.

I have undertaken numerous data science projects most recently with a focus on Unsupervised learning for computer vision. Using frameworks like PyTorch and Tensorflow, I have developed deep clustering models which enable test engineers to gain early insight into unlabelled datasets.

Previous to my interest in computer vision, I built a Sentiment Analysis applciation that leveraged Natural Language Processing (NLP) techniques and the Twitter API to gain insight into current public sentiment of topics.

I am extremely proficient in software development particularly using Python and Java having studied Object-Orientated Programming (OOP) concepts at both college and University level, but my curiosity leads me towards learning and experimenting with different technologies and programming languages, further expanding my understanding of Object-Oreintated programming.

  • Birthday: February 2000
  • Website: markjmcallister.com
  • Phone: Provided Upon Request
  • City: Glasgow, Scotland
  • Age: 24
  • Degree: MSc Artificial Intelligence with Distinction
  • Email: contact@markjmcallister.com
  • Current Position: Software Developer at HM Government

Besides work and studies, I'm a huge fan of Football and Golf and love to play both as much as I can. I also like to travel. You can see me standing atop the summit of Jungfrau mountain in Switzerland as the websites header photograph.

Skills

Below is a concise summary of some of the technologies and frameworks with which I am proficient. I have also included a subjective evaluation of my familiarity with these technologies.

Python 100%
Java 100%
Object-Orientated Programming 100%
SQL 80%
R 65%
PyTorch 90%
Tensorflow 75%
NumPY, Pandas 75%
NoSQL, MongoDB 70%
Amazon Web Services (AWS) 60%

CV

Sumary

Mark McAllister

Innovative and deadline-driven Data Scientist and Software Developer with one years industrial experiance and over 5 years academic experiance in Data Science and SDLC Concepts.

  • Glasgow, Scotland
  • Phone Number available upon request
  • contact@markjmcallister.com

Education

Master of Science Artificial Intelligence

2021 - 2022 Grade: Distinction

University of Stirling, Stirling, Scotland

Within my master's degree, I have learned to apply artificial intelligence solutions to real-world problems and commercial tasks, and I have gained hands-on experience with deep-learning based computer vision and natural language processing algorithms

Bachelor of Science (Honours) Computing Science

2019 - 2021 Grade: First Class

University of Stirling, Stirling, Scotland

I expanded my knowledge of computing science principles by learning a variety of aspects such as distributed and parallel computing, cyber security and web services. I developed a particular interest in Artificial intelligence, for my final year honours project, I designed and developed a sentiment analysis web application utilising machine learning frameworks like Tensor Flow and the Natural Language Toolkit.

Professional Experience

Data Science Intern

2022 - Present

ST Microelectronics, Edinburgh, Scotland

  • Lead in the design, development, and implementation of Unsupervised Clustering models to gain insight into unlabelled datasets.
  • Consulted with international teams on effective methods to organise and store complex datasets, eventually developing a HDF5 storage solution to drastically improve query time.
  • Participated in the development and testing of an in-house software tool for data visualisation and iterative learning.
  • Regularly presented project updates and presentations to the wider organisation, effectively explaining technichal conepts to a non-technical audience.

Sales Associate

2017 - 2022

Tesco Mobile, Glasgow, Scotland

  • Working in a high-pressure sales environment, providing a high level of customer service while maintaining individual and organisational sales targets
  • Since joining, I have exceeded individual sales targets for the last four years and assisted the store in meeting sales targets for the previous two years
  • Provided technical support and troubleshooting assistance to customers, resolving issues quickly and effectively
  • Maintained in-depth knowledge of the latest mobile phone models and features, enabling effective product recommendations and upselling

Projects

Below is a brief summary of some of the projects I have contributed to and participated in.

Optimized HDF5 Data Storage Solution

Implemented an efficient data storage solution using the HDF5 file structure, significantly improving I/O operations and supporting seamless data management across the organisation.

Innovative Iterative Learning Tool

Developed a user-friendly learning tool leveraging PyTorch and FastAI, streamlining the process of building machine learning models by enabling users to label data and retrain Convolutional Neural Networks (CNN).

Deep Adaptive Clustering for Computer Vision

Explored the potential of Deep Adaptive Clustering models to enhance unsupervised learning methods in computer vision, enabling test engineers early insights into new and unlabelled datasets.

SentiSearch: Sentiment Analysis Web Application

Created a full-stack web application using Django, NLTK, and AWS Comprehend, enabling users to analyze Twitter sentiment through natural language processing techniques.

Yorkies Vehicle Hire: Comprehensive Booking System

Developed a full-stack vehicle hire booking system using ASP.Net, HTML/CSS, Bootstrap and SQL Server which could facilitate online bookings and process payments with Stripe and PayPal

Real-Time Anomaly Detection in Time Series Data

Designed and implemented a real-time anomaly detection system using machine learning algorithms, such as Isolation Forest and Autoencoders, to identify unusual patterns in time series data.