Janis Klaise
Head Of Data Science
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJanis Klaise is a Head of Data Science with over a decade of experience building and leading ML teams to productionize cutting-edge AI, currently driving data strategy at Quorso after senior leadership roles at Seldon. He holds a PhD in Mathematics and Complexity Science from the University of Warwick and leverages deep expertise in complex networks and mathematical modelling to design robust, explainable ML systems. Janis combines hands-on engineering—contributions to well-known open-source MLOps projects like Seldon Core and explainability/drift tools such as Alibi—with product-focused leadership that spans monitoring, deployment and model governance. His background in both research and DevOps makes him comfortable moving models from novel algorithms to scalable Kubernetes-based production. Notably, he’s contributed core fixes and examples that improved Python 3 compatibility, TensorFlow payload support and documentation/CI for community ML tooling.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics and Complexity Science, Doctor of Philosophy (Ph.D.) Mathematics and Complexity Science at University of Warwick
English, Latvian
Github Skills (22)
graph-algorithms10
python10
sphinx10
machine-learning10
cicd10
mlops10
complex-networks10
documentation10
data-analysis10
unit-testing9
datatable9
tabular9
tensorflow9
datatables9
jupyter-notebook9
Programming languages (10)
Github contributions (5)
SeldonIO/alibi
Feb 2019 - Jan 2023
Algorithms for explaining machine learning models
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:30 releases, 781 reviews, 430 commits in 3 years 11 months
Contributions summary:Janis primarily focused on setting up the project structure, including the package skeleton and the initial documentation configuration. They implemented the initial configuration for Sphinx documentation, as well as the supporting configurations for flake8 and CI pipelines. Additionally, the user was instrumental in adding supporting features for rendering notebooks.
fairness-mlpythoninterpretabilityxaiexplanations
SeldonIO/alibi-detect
Oct 2019 - Jan 2023
Algorithms for outlier, adversarial and drift detection
Role in this project:ML Engineer
ML EngineerContributions:9 releases, 668 reviews, 106 commits in 3 years 3 months
Contributions summary:Janis's primary contribution involves adding the `DataTracker` class and its usage example within a Jupyter Notebook. This example demonstrates how to track and visualize data metrics for tabular data, incorporating the `alibi.datasets` library for dataset loading. The user's work is focused on data analysis and feature engineering within the context of an outlier detection framework.
benchmarkingadversarialdrifttime-seriesoutlier-detection
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