Anjum Sayed
Lead Data Scientist
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolAnjum Sayed is a Lead Data Scientist with over a decade of cross-domain experience applying state-of-the-art deep learning and classical ML to energy, motorsport and finance problems. A former petrophysicist at bp, he built scalable automated workflows across upstream, downstream and renewables, often turning decades of messy data into production-ready models that drove measurable operational gains. Now at McLaren Racing he develops predictive tools that help win championships, while also founding Tapesearch to explore new data-driven ventures. A top-ranked competitive data scientist (multiple 1st and 2nd place finishes) he brings practical expertise in PyTorch, TensorFlow, XGBoost, RAPIDS and cloud ML, plus web and database skills to ship end-to-end solutions. He contributes to notable open-source projects—improving financial analytics in the ffn library and adding leakage-safe encoders to scikit-learn-contrib—demonstrating attention to robustness and reproducibility. His background in particle physics and field experience across challenging oilfields give him an uncommon blend of theoretical rigor and pragmatic engineering.
11 years of coding experience
3 years of employment as a software developer
University College London
Stackoverflow
884reputation
184kreached
15answers
10questions
Github Skills (22)
performance-monitor10
python10
scikit10
finance10
pandas10
numpy10
statistical-models10
cross-validation10
scikit-learn10
modeling10
metric10
data-analysis10
data-science9
machine-learning9
computer-engineering8
Programming languages (10)
Github contributions (5)
scikit-learn-contrib/category_encoders
Mar 2020 - Mar 2020
A library of sklearn compatible categorical variable encoders
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 2 PRs, 7 comments in 21 days
Contributions summary:Anjum primarily contributed to the `category_encoders` library by adding a `NestedCVWrapper` class to prevent target leakage within supervised encoders. The user modified the code and documentation and implemented methods to ensure compatibility with the existing encoder structure. They also added unit tests, and ensured correct functionality with custom cross-validation methods.
sklearn
pmorissette/ffn
May 2016 - May 2016
ffn - a financial function library for Python
Role in this project:Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:8 commits, 3 PRs, 1 comment in 1 day
Contributions summary:Anjum's primary contribution involves enhancing the `ffn` library with financial functions, specifically focusing on the implementation of Calmar and Sortino ratios. The commits demonstrate the addition of these performance metrics across daily, monthly, and yearly timeframes within the `PerformanceStats` class, showcasing a focus on financial analysis. The code modifications also incorporate bug fixes related to the calculation of downside risk and refine the organization of displayed statistics.
python
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