Guzal Bulatova
Data Scientist
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolGuzal Bulatova is a Data Scientist based in London with five years’ experience translating machine learning research into enterprise-scale AI products. At Haleon she led a cross-functional team to deliver an AI translation platform used by 20,000+ employees across 44 markets and drove influenza forecasting and platform initiatives that yielded multimillion-dollar savings and major process automation. She bridges technical delivery and strategic decision-making—setting governance, building business cases for C-suite stakeholders, and guiding feasibility for company-wide AI expansion. An active open-source contributor to sktime, she implemented time-series transformations (including Guerrero Box-Cox lambda estimation) and a ThetaLines transformer, reflecting strong applied expertise in forecasting. Her background in translation and multilingual studies underpins a practical sensitivity to localization and cross-cultural stakeholder alignment that has powered global deployments.
5 years of coding experience
1 year of employment as a software developer
Higher Vocational Education, Object-oriented System Development, Higher Vocational Education, Object-oriented System Development at HandelsakademinMaster's degree, Language Interpretation and Translation, Distinction, Master's degree, Language Interpretation and Translation, Distinction at Saint Petersburg State UniversitySvenska som andra språk (SVA3), A, Svenska som andra språk (SVA3), A at Hermods AB
Spanish, Russian, English, Swedish, Tatar, French
Github Skills (10)
scikit10
forecasting10
pandas10
machine-learning10
time-series10
forecast10
python10
numpy10
scikit-learn10
sktime8
Programming languages (4)
Github contributions (5)
sktime/sktime
May 2021 - Nov 2022
A unified framework for machine learning with time series
Role in this project:
Data Scientist
Data ScientistContributions:127 reviews, 13 commits, 22 PRs in 1 year 6 months
Contributions summary:Guzal significantly contributed to the `sktime` repository by implementing and refining time series transformation methods. Their work focused on the Box-Cox transformation, adding and optimizing the Guerrero method for lambda estimation. They also addressed documentation and testing aspects, demonstrating a commitment to code quality and usability. Additionally, the user created a new ThetaLines transformer for time series decomposition.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
GuzalBulatova/sktime
Apr 2021 - Feb 2023
A unified framework for machine learning with time series
Contributions:246 pushes, 27 branches in 1 year 10 months
deep-learningtime-seriesmachine-learning
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