RICHARD ADEMEFUN
London,City of London
Summary
Accomplished technical Data Science leader with expertise in Python programming and proficiency in SQL and MongoDB. Demonstrates strong capabilities in technical leadership and mentorship, fostering growth through test-driven development practices. Skilled in distributed computing tools and cloud computing with AWS and Azure, leveraging machine learning frameworks to drive innovation. Familiarity with Java, Rust, and Go complements a robust understanding of Agile Scrum methodology. Committed to advancing career goals by integrating cutting-edge technologies into scalable solutions.
Overview
6
6
years of professional experience
Work history
Senior Data Scientist
Shell
London, United Kingdom
09.2023 - 09.2025
- Developed predictive time-series models using economic indicators to extract actionable insights.
- Simulated model impacts on business metrics, optimising decision-making processes.
- Created condition monitoring models for lubricant data to enhance machine efficiency in B2C sector.
- Processed large-scale datasets using Databricks and PySpark to streamline workflows.
- Produced reports with Plotly Dash to improve model explainability for stakeholders.
- Led projects from ideation to deployment as a solo developer, ensuring successful delivery.
- Guided two data scientists in developing profitable ML and GenAI products.
- Organised cultural committee events, including external speakers and internal GenAI hackathons.
Senior Data Scientist
Aubay
London
05.2022 - 09.2023
- Collaborated with Shell’s lubricants marketing department to develop data-driven product for evidence-based claims.
- Deployed predictive solution with global lubricants technical team, utilising lower frequency data for B2C applications.
- Provided relevant inference code in Java to facilitate product deployment on existing platform.
- Utilised diverse tech stack, including Azure blobs and Databricks, to create a dynamic claims dashboard.
- Generated product claims based on real and regression data within the dashboard environment.
- Worked effectively in large, agile teams comprising UX/UI designers and data engineers.
- Demonstrated leadership by mentoring less senior team members across various projects.
Data Scientist
The Floow
Sheffield
01.2020 - 05.2022
- Leveraged telematics data to develop features that enabled insurers to evaluate user driving behaviour.
- Conducted data preprocessing and trained models including XGBoost and Lasso Regression.
- Employed SHAP analysis for clear interpretation of non-linear model behaviours.
- Analysed large datasets using Java, R, Python, and AWS services for optimal results.
- Demonstrated object-oriented programming expertise, ensuring code quality through unit testing.
- Regularly communicated insights to clients and stakeholders, enhancing project visibility.
- Collaborated effectively within agile teams, working closely with UX/UI designers and developers.
- Provided mentorship to new team members, supporting their professional development.
R&D Scientist
BioEpic
Hereford
05.2019 - 10.2019
- Extracted features from signals to assess health and model physiology for Cardio team.
- Conducted data analysis and visualisation using MATLAB and Python on large datasets.
- Developed functions and codes, ensuring functionality through unit testing in Git repository.
- Utilised TensorFlow-GPU and Keras libraries to enhance machine learning applications.
- Authored literature reviews and prepared comprehensive reports to effectively communicate findings.
Education
Mres - BioEngineering
Imperial College London
09.2018 -
BENG - Medical Engineering
Swansea University
06.2017 -
Skills
- Python programming expertise
- Java, Rust, and Go (beginner)
- SQL and MongoDB proficiency
- Technical leadership and mentorship
- Test-driven development practices
- Distributed computing tools
- Cloud computing with AWS and Azure
- Machine learning frameworks
- Agile Scrum methodology
Publications
- Ding Z, Güdel M, Smith S, Ademefun R, Bull AMJ. Measuring Three-dimensional Knee Kinematics Using a Femoral Clamp: Accuracy, Repeatability and Reproducibility in Gait. J Biomech Eng. 2019 Oct 1. doi: 10.1115/1.4045115. Epub ahead of print. PMID: 31596924.
- Gazze, A, Ademefun, R., Conlan, R. & Teixeira, S. (2018) Electrochemical impedance spectroscopy enabled CA125 detection; toward early ovarian cancer diagnosis using graphene biosensors. Journal of Interdisciplinary Nanomedicine. [Online] Available from: doi:10.1002/jin2.40
Timeline
Senior Data Scientist
Shell
09.2023 - 09.2025
Senior Data Scientist
Aubay
05.2022 - 09.2023
Data Scientist
The Floow
01.2020 - 05.2022
R&D Scientist
BioEpic
05.2019 - 10.2019
Mres - BioEngineering
Imperial College London
09.2018 -
BENG - Medical Engineering
Swansea University
06.2017 -
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