Ryan Nazareth
Senior Data MLops Engineer
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolRyan Nazareth is a Senior Data/MLops Engineer based in London with 10 years’ experience building production ML pipelines, monitoring model drift, and translating research-grade time series and Bayesian models into robust enterprise systems. He blends hands-on engineering at firms like Lloyds and Entain with active open-source contributions to high-profile projects such as pandas, Prophet and PyCaret—improving time series plotting, uncertainty handling, and MLflow experiment logging. Comfortable across cloud and on-prem platforms, he is fluent in Python, Airflow, Docker, SQL and graph‑based probabilistic modelling, and he mentors teams on CI/CD and reproducible ML. An AWS Community Builder and occasional DeepRacer competitor, he pairs practical MLOps delivery with a scholarly interest in Bayesian inference and NLP, making him equally at home prototyping novel models or hardening them for scale.
10 years of coding experience
6 years of employment as a software developer
MSc, Biomedical Engineering, Pass with Merit, MSc, Biomedical Engineering, Pass with Merit at Imperial College LondonMSc, Data Science, Distinction, MSc, Data Science, Distinction at City University LondonBSc, Natural Sciences, First class honours, BSc, Natural Sciences, First class honours at University College London, U. of London
English
Github Skills (20)
pycaret10
python10
data-science10
r10
testing10
pandas10
machine-learning10
time-series10
mlflow10
forecasting10
cross-validation10
forecast10
plotly10
documentation10
data-analysis10
Programming languages (3)
Github contributions (5)
facebook/prophet
Oct 2019 - Jun 2020
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Role in this project:
Data Scientist
Data ScientistContributions:23 commits, 19 PRs, 79 comments in 8 months
Contributions summary:Ryan primarily contributed to the enhancement of the Prophet time series forecasting tool. Their work focused on improving the handling of uncertainty in forecasts, specifically by adding options to disable the calculation and plotting of uncertainties. They addressed several syntax issues and adapted the cross-validation process and performance metrics to properly handle models with or without uncertainty samples. Additionally, they added tests to verify the implemented changes and addressed an issue to suppress plotly errors, updating a notebook for improved plotting.
forecastingpythontime-series-analysisforecasting-modelsforecasts
pandas-dev/pandas
Jun 2018 - Feb 2020
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:Technical Writer / Documentation Specialist
Technical Writer / Documentation SpecialistContributions:21 commits, 31 PRs, 184 comments in 1 year 8 months
Contributions summary:Ryan primarily contributed to improving the documentation within the pandas library. Their work involved updating and correcting docstrings for various functions, including `str.rsplit`, `to_timedelta`, and `to_stata`. Additionally, they addressed deprecation warnings and improved the clarity of documentation. Their edits focused on enhancing the user experience and providing accurate information about the library's functionalities.
pythondatalabeled-datamanipulationdataframes
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