Abdirisak Ali
Summary
BSc Computer Science and MSc Artificial Intelligence graduate with proven commercial experience building production data pipelines, automated validation systems, and cloud-based data infrastructure. At INRIX, I ingested and processed high-volume real-time data from multiple simultaneous live sources, independently diagnosed and resolved a production pipeline fault under time pressure, and built Python QA scripts that improved data accuracy across the team. At BlackRock, I delivered production-grade Java automation within the Aladdin investment management platform in a high-compliance environment. I have hands-on experience with Azure, including Azure Data Factory, Azure Machine Learning, Azure Blob Storage, App Services, and Virtual Machine infrastructure. I also built an end-to-end NLP pipeline analysing approximately 800,000 Twitter posts about football transfers and clubs, extracting player and club entities, modelling fan sentiment using BERT-based transformers, and identifying engagement trends across a full transfer window. Proficient in Python, SQL, GCP, BigQuery, Azure, Databricks, Docker, Git, and CI/CD. A committed Manchester United supporter with a genuine understanding of the club's data-driven performance culture.
Overview
2
2
years of professional experience
4
4
years of post-secondary education
Work history
Traffic Data Analyst
INRIX
Birmingham, UK
2023.09 - 2024.12
- Built and maintained production data pipelines ingesting high-volume real-time data simultaneously from social media feeds, CCTV systems, police scanners, and highway authority sources, processing multiple concurrent live streams with near-perfect accuracy for broadcast clients across the UK and North America.
- Independently identified a live pipeline fault where outdated records were surfacing ahead of current data. Diagnosed the root cause, wrote and tested a Python fix, and deployed it under time pressure, immediately restoring data integrity. Communicated the issue and resolution clearly to both technical colleagues and non-technical stakeholders.
- Built Python scripts for automated data validation and QA, enforcing data quality standards and reducing manual checking by 5 or more hours per week, embedding systematic monitoring and testing into the team's production workflow.
Aladdin Engineering (Summer Internship)
BlackRock
Edinburgh, UK
2023.06 - 2023.08
- Delivered production Java automation scripts within the Aladdin investment management platform, eliminating 51 hours of manual work per week across six recurring operational workflows in a high-compliance, high-reliability environment with zero tolerance for pipeline failures.
- Collaborated with senior engineers and global stakeholders to gather requirements, translate them into well-engineered technical solutions, and produce clear documentation for every process built, directly mirroring the cross-functional working between Football Data Engineers, analysts, and performance staff at Manchester United.
- Embedded secure coding practices and data governance standards throughout, operating within strict compliance frameworks where data accuracy and pipeline reliability were business critical.
Education
MSc - Artificial Intelligence & Sustainable Development
University of Birmingham
Birmingham 2024.10 - 2025.12
BSc (Hons) - Computer Science
Birmingham City University
Birmingham 2021.09 - 2024.07
Skills
Languages: Python, SQL, Java, JavaScript, TypeScript, C, Scala
Pipeline: ETL pipeline design, data ingestion, data quality and validation, automated monitoring, API integration
Modelling: Dimensional modelling, star schema, fact/dim tables, data warehousing, data modelling
Cloud: Azure (Data Factory, Azure ML, Blob Storage, App Services, Virtual Machines), GCP, BigQuery, AWS, Databricks, Snowflake (familiarity)
Engineering: Git, GitHub Actions, CI/CD, Docker, REST APIs, Nodejs, PostgreSQL, Supabase
NLP and ML: BERT, Hugging Face Transformers, PyTorch, spaCy, TF-IDF, LDA, Scikit-learn, XGBoost, Gradient Boosting, LSTM, Pandas, NumPy
Tools: Power BI, Looker Studio, Matplotlib, Seaborn, Jupyter, JIRA, Alteryx
KEY PROJECTS
Twitter Sentiment and NLP Analysis of Football Transfers | Personal Project Jul 2025 – Sep 2025
- Built an end-to-end NLP pipeline to analyse approximately 800,000 Twitter posts related to football clubs and transfers across a full summer transfer window. Performed extensive data cleaning, feature engineering, and time-series preprocessing on social media data at scale.
- Implemented sentiment analysis using both polarity-based methods and BERT-based transformer models (Hugging Face), alongside toxicity detection to identify harmful or discriminatory content. Applied TF-IDF and Latent Dirichlet Allocation (LDA) for topic modelling, identifying key themes including transfers, match results, and club discussions.
- Used spaCy for Named Entity Recognition and POS tagging to extract players, clubs, and contextual language patterns from unstructured text. Generated visual insights using Matplotlib, Seaborn, and WordClouds, uncovering fan sentiment trends, tweet volume spikes, and engagement patterns across the transfer window. Technologies: Python, Pandas, NumPy, spaCy, Scikit-learn, Hugging Face Transformers, PyTorch.
Languages
English
Fluent
Somali
Native
Timeline
MSc - Artificial Intelligence & Sustainable Development
University of Birmingham
2024.10 - 2025.12
Traffic Data Analyst
INRIX
2023.09 - 2024.12
Aladdin Engineering (Summer Internship)
BlackRock
2023.06 - 2023.08
BSc (Hons) - Computer Science
Birmingham City University
2021.09 - 2024.07