Ryan Mottram

A hardworking and enthusiastic developer with a main area of interest and knowledge in machine learning and software engineering.

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About
Ryan Mottram

I'm a Machine Learning and Software Engineering enthusiast from Bradwell, Norfolk, with programming, algorithmic and problem-solving skills across data science, machine learning and software development. I'm always eager to expand my knowledge and take on new challenges in technology.

I currently work as a Data Analyst at BT (Adastral Park), where I own the automated procedures that calculate KPIs and help maintain the Python ETL pipelines behind BT & EE device registration. Pulling data from GCP, transforming it, and loading it into BigQuery for dashboards and reports. Alongside this I'm building my AI and machine learning skills on AWS, working hands on with SageMaker, Amazon Q Developer and Lambda.

Outside of work I build my own iOS apps and I always have a backlog of Lego sets where I usually build one every two months.

Currently

  • Data Analyst at BT, Adastral Park
  • September 2022 - present
  • Python ETL · BigQuery · GCP
  • Upskilling in AWS AI services

Education

  • MRes Machine Learning (Pass)
  • BSc (Hons) Computer Science (2:1)
  • University of Lincoln, UK
  • Research: ML/DL intrusion detection, 95 - 99% accuracy

Certifications

  • Microsoft Azure AI Fundamentals
  • MTA Database Administration Fundamentals
  • NLP with Python for ML - Essential Training
Python Swift C# SQL TensorFlow Keras scikit-learn Pandas OpenCV SwiftUI Django FastAPI MySQL AWS GCP Git · CI/CD
Portfolio

Things I've built

A few of the projects I've worked on, take a look and make your own opinion.

JuiceBox

My most ambitious side project, built after buying my first EV and wanting to track running costs properly. What started as a personal tool grew into a published App Store app with nearly 200 downloads and paying subscribers. Version 1.3, out now, adds Live Activities, peak and off-peak tariffs, solar and home battery costing, running cost reminders and employer reimbursement reports, alongside on-device machine learning predictions and an Apple Watch app.

Technologies

  • Xcode
  • SwiftUI
  • SwiftData
  • Core ML
  • CloudKit
  • RevenueCat
  • FeaturePulse
  • PostHog

Features

  • Charge time & cost tools
  • Usage & cost charts
  • Battery health estimates
  • Apple Watch app
  • iCloud sync & backup
  • Light & dark themes
  • Live in-app news
  • Live Activities 1.3
  • Peak & off-peak tariffs 1.3
  • Solar & home battery 1.3
  • Running costs 1.3
  • Reimbursement reports 1.3
  • Fuel savings 1.3

Wisppad

A notes app built around the idea of impermanence. Notes automatically delete after a set time, keeping things from accumulating. Inspired by my own habit of piling up forgotten notes while building JuiceBox. Wisppad supports a range of note types including maths, code snippets, checklists, voice, handwriting, and camera OCR. Notes that are approaching deletion visibly fade out before wisping away. Users can pin notes indefinitely by pinching them, though Wisp, your in-app companion, won't be pleased about it.

Technologies

  • Xcode
  • SwiftUI
  • SwiftData
  • PencilKit
  • CloudKit
  • RevenueCat
  • PostHog

Features

  • Pinch & swipe gestures
  • Keyword note types
  • Per-note timers
  • Smart maths & conversions
  • Live in-app news
  • iCloud sync

Other Stuff

A few other projects either during university, old projects I worked on or sub-projects.

University Project

My final year project: an iOS app that identifies mobile vulnerabilities and reports them to the user - combining mobile development and cyber security. It checks for weak passwords, unsecured WiFi and open ports, connected Bluetooth devices, and scans files for potentially malicious content.

Technologies

  • Visual Studio
  • Xamarin.iOS in C#
  • UIKit
  • CloudKit
  • CoreBluetooth
  • MobileCoreServices

Features

  • Password strength checker
  • Port scanner
  • File checker
  • Bluetooth scanner
  • Safety education

Where's Wally?

My second year Object Oriented Programming assignment: used to apply OOP principles to programmatically find Wally in a black and white image. The program cross-references the pixel values of a Wally reference against the scene, then outputs the scene with Wally's likely location shaded.

Technologies

  • Visual Studio
  • C++
  • 2D Matrices
  • Polymorphism

Features

  • Finds Wally in the scene
  • Where's Wally facts
Contact

Get in touch

My other projects and work are on GitHub. Say hello on Instagram or LinkedIn.