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Data Consultant (Data Scientist)

WSP

Client-facing data science and data engineering across the full delivery lifecycle for infrastructure clients.

• Deliver client-facing analytics for infrastructure clients across the full delivery lifecycle: engage stakeholders to gather requirements and determine root causes, design solutions, develop and test in Python and SQL, and present outcomes to technical and non-technical audiences.
• Design and implement the ETL/ML pipeline for structures asset valuation on Spark, Databricks and Azure, deploying predictive models end to end with automated testing, model evaluation and monitoring frameworks, and CI/CD.
• Interrogate millions of asset records using SQL and Python, replacing a manual, laptop-dependent process with a ~20-minute automated workflow feeding downstream valuation and predictive-maintenance models.
• Engineer NLP pipelines and statistical models over unstructured free-text data on structures (transformers, embeddings, TF-IDF), automating critical analysis workflows with 10/10 client feedback.
• On the East West Rail Digital Spine Programme (MWJV), develop ETL pipelines integrating DOORS requirements data into the Digital Spine and own the semantic layer architecture, defining common business entities and data models for cross-system traceability across Azure, PostgreSQL and Microsoft Fabric.
• Engineer an Azure Function App in C# (HTTP-triggered, managed identity), hardening cross-system data movement.
• Deliver Power BI dashboards, reporting and user training so non-technical stakeholders and executives monitor outputs self-serve.
• Coach and mentor junior team members through code reviews and knowledge sharing, and provide input to tenders and bids.

Graduate Data Consultant

WSP

Data engineering and business analytics across multiple infrastructure sectors.

• Developed large-scale analytical pipelines in Python and R linking defects to recommendations and fast-tracking engineering decisions on Network Rail and National Highways work.
• Implemented data quality assurance and metadata management frameworks ensuring compliance with data governance and regulatory requirements.
• Designed and implemented transport data pipelines for Transport for West Midlands (TfWM) in multidisciplinary delivery teams.
• Contributed as a Data Engineer to Transport for Wales (TfWRL) commercial analytics and service performance integration into Azure, delivering Power BI training so end users could extract and analyse commercial insights self-serve.

MSc

University of West London

Artificial Intelligence (Computer Vision)

• Computer Vision
• Deep Learning Architectures
• Advanced Machine Learning
• Statistical Methods
• Data Mining
• Natural Language Processing
• Research Methodologies

BSc (Hons)

Sudan University of Science and Technology

Electrical Engineering, specialisation in Control Theory

Distributed Geospatial Information Systems Challenges and Opportunities

Exploring Remote Sensing - Methods and Applications

Co-authored paper discussing challenges and opportunities in Distributed Geospatial Information Systems.

National Highways Asset Management System (AMS) Data Migration and Transformation

WSP

• Deliver data transformation and migration workstreams, developing Python, pandas and GeoPandas based solutions to reconcile asset registers and datasets across multiple source systems.
• Design automated data quality, conflict-resolution and reporting workflows, including dependency analysis, change tracking and exception management, reducing manual effort and improving confidence in AMS-ready datasets.

Energy Demand Forecasting, Northern Powergrid x Newcastle University

Turing Institute Data Study Group

• Performed exploratory analysis on electricity distribution network data, identifying cost trends and proposing a time-series forecasting model for energy demand to support net-zero transition planning.
• Delivered reproducible analysis in Jupyter Notebooks with GitHub, collaborating with the network's operational architects.

Wind Turbine Surface Defects Detection

University of West London

• MSc dissertation developing a deep learning based computer vision algorithm for automated wind turbine surface defect detection, applying convolutional neural networks and transfer learning with rigorous performance evaluation.