Hello.
My name is Alex Moscrop.
I am a data services professional with over a decade’s experience solving complex problems and helping people make evidence-based decisions.
Software Engineering
Python | Flask | Socket.IO | JavaScript | HTML | CSS | SQL | Docker | Kubernetes | Linux | Ubuntu
Alpine F1, a leading Formula One team, faced challenges with their stress simulation workflows, relying on manual command-line submissions and fragmented processes. Seeking a solution to enhance efficiency and control, they engaged with me to modernise their workflow management system. Understanding the need for real-time insights and streamlined processes, I developed a Flask-based web application linked to a SQL database, containerised and deployed via Kubernetes.
The implemented solution revolutionised Alpine F1's workflow management. Engineers now access a user-friendly interface offering real-time monitoring of cluster and license usage. Centralised job management functionalities enable seamless submission, tracking, and control of stress simulation jobs.
The impact of the new system was profound. By eliminating manual steps and providing comprehensive visibility, the solution significantly reduced job submission times, empowered engineers with greater control, and facilitated informed decision-making. Engineers now spend less time on administrative tasks, allowing them to focus on critical engineering work. Real-time insights into resource utilisation have enabled proactive resource optimisation, while management can make informed decisions to ensure efficient resource allocation. Overall, the collaboration with Alpine F1 resulted in a transformational improvement in stress simulation workflows, positioning them for continued success in Formula One racing.
Machine Learning
Python | TensorFlow | Selenium | SQL | QGIS | C++ | PineScript | EasyLanguage
Driven by a passion for investment and the pursuit of financial independence, in 2021 I embarked on a series of projects within the finance and investment space, coinciding with the establishment of my contracting company, Fire Analytics.
Traditionally, UK property investment relies heavily on manual asset valuations due to the lack of publicly available data for estimating core performance indicators, such as rental yield. To address this, I developed a sophisticated algorithm capable of periodically gathering rental data from major property brokers. Leveraging this data, I trained a neural network to accurately predict return-on-investment (ROI), creating an investment map overlaid with performance metrics to empower investment identification and selection.
Additionally, recognising the potential for diversification to mitigate long-term investment risk, I leveraged over 30 years of commodity market data and combined it with proprietary broker languages and C++ to develop a series of trading algorithms, each tailored to a diverse range of market conditions. Through rigorous back-testing, carefully balancing return, drawdown and statistical benchmarking, I now maintain a suite of profitable strategies that operate 24/7, providing a continuous, risk-adjusted income stream.
Data Modelling
SQL | Snowflake | Python | Jupyter | Regression | Matplotlib | Qlik | PowerBI
A prominent publishing and education company, John Wiley & Sons faced significant challenges in achieving an accurate and comprehensive view of business performance. Tracking key performance indicators (KPIs) was hindered by gaps in data coverage and inadequate quality control caused inconsistencies in how metrics were calculated and reported.
Coinciding with the introduction of a new datalake, I re-engineered ELT (Extract, Load, Transform) procedures from the ground up. Implementing rigorous quality assurance at each stage of data transformation, from source to report-ready tables, ensured reliability and accuracy, addressing the organisation's longstanding challenges with data integrity and provided a solid foundation for performance evaluation.
In tandem, I established an extensive self-serve reporting ecosystem, designed to deliver quick and reliable information to key decision-makers and empower stakeholders with actionable insights. This culminated in the deployment of prediction algorithms to forecast critical business metrics, an invaluable resource for the executive leadership team, offering foresight into business trends and guiding the strategic decision-making process.
Experience
Data Modeller
Freelance
Jul 2023 - Present
Freelancing for data science, engineering and analytical projects. Recent works include:
Full stack development of a web application to manage stress engineering workflows and resource usage for a leading F1 team (Python, Flask, Socket.IO, JavaScript, HTML, CSS, SQL, Docker, Kubernetes, Linux, Ubuntu).
Prediction of financial performance for UK property investments through the sourcing of purchase and rental data, combined with supervised learning techniques and geospatial visualisations (Python, Selenium, TensorFlow, QGIS).
Dashboarding of macro-economic trends covering multiple developed economies through the ETL of market and financial data for a large industrial manufacturer (Python, SSIS, SSMS, PowerBI).
Automation of algorithmic trading models for equity and commodity futures, parameter tuned to fit risk profiles and objective functions (Python, PineScript, Easy Language).
Senior Data Modeller
John Wiley & Sons
Jun 2016 - Jul 2021
Accountability for large-scale analytical projects, pipelines, and the deployment of new technologies. Notable achievements:
Predictive modelling of business-critical trends (Python, Regression, Qlik, SQL).
Harmonisation of fractured data models through pattern recognition and clusterisation techniques (Python, Dedupe, Matplotlib, SQL).
Enrolled on a program designed to fast-track career development and facilitate mentorship from senior leadership.
Marketing Operations Analyst
Barclaycard
Jun 2015 - Apr 2016
Data extraction, preparation and filtering for direct marketing campaigns. Development and maintenance of automation and integration with campaign delivery platforms. Evaluation of campaign performance and presentation of findings to senior stakeholders with recommendations for post-campaign improvements.
Marketing Executive
Sterling
Feb 2012 - Jun 2015
Ideation, creation and delivery of marketing campaigns. Production of marketing materials and distribution to sales teams. Establishment of central customer data repositories. Mentoring graduate trainees. Responsibility for tender submissions.
Education
Deep Learning (Cert.)
DeepLeanring.AI
Oct 2021 - Dec 2022
Deep learning specialisation using transformers, convolutional and recurrent neural networks to solve natural language, computer vision and predictive problems. Focus on structuring large-scale machine learning projects and performance tuning.
Machine Learning (Cert.)
Stanford University
Jun 2021 - Oct 2021
Strategic Marketing (MSc)
Coventry University
Sep 2008 - Jun 2009
Broad introduction to machine learning, statistical pattern recognition, fundamental functions and mathematics for supervised and unsupervised learning techniques.
Business Information Technology (BSc)
Coventry University
Sep 2005 - Jun 2008
Overview of marketing theory and practice. Creation and implementation of strategic marketing plans. Conducting market research and analysing survey results. Fundamentals of statistics with additional focus on the use of factor analysis.
Introduction to business management theory and practice. Overview of information systems, programming and networking. Introduction to Python. Introduction to accounting and finance. Introduction to marketing.