Maren Eckhoff
Director Of Applied AI Science & Engineering at Ellison Institute of Technology Oxford
Oxford, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolMaren Eckhoff is a technology leader and Director of Applied AI Science & Engineering with over a decade of machine learning experience and a PhD in Mathematics. She has led Scientific AI programmes at QuantumBlack/ McKinsey, partnering with top pharma and biotech firms to accelerate R&D, optimize clinical trials, and discover new modalities using real-world evidence, knowledge graphs, and generative AI. Previously she built forecasting systems at Tesco that delivered multi-million pound savings through improved demand prediction and seasonality modelling. Maren combines deep mathematical rigour from academic research in probability and networks with practical expertise in MLOps and reusable ML pipelines deployed across 100+ projects. She is particularly skilled at translating novel research into robust, scalable production assets that unlock new business questions. Based in Oxford, she now focuses on commercially sustainable AI solutions tackling large scientific challenges.
10 years of coding experience
10 years of employment as a software developer
Diplom Mathematics, Diplom Mathematics at Technical University of MunichAbitur - General Qualification for University Entrance, Abitur - General Qualification for University Entrance at Gymnasium MeckelfeldExchange Mathematical Finance and Economics, Exchange Mathematical Finance and Economics at National University of Singapore
English, German
Stackoverflow
1reputation
0reached
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Github Skills (36)
rows8
science8
machine-learning8
javascript8
data-science8
statistical-inference7
memory-mapped-file7
probabilistic7
mcmc7
bayesian7
python7
bayesian-inference7
modeling6
tabular6
probabilistic-models6
Programming languages (5)
Github contributions (5)
Maren-Eckhoff/website
Jul 2018 - Aug 2018
Contributions:2 releases, 4 PRs, 18 pushes in 14 days
Maren-Eckhoff/Anaconda
Apr 2016 - Jun 2016
Contributions:4 pushes, 1 branch in 1 month
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