Jens Wahl
Senior Data Scientist II
Vancouver, British Columbia, Canada
Summary
👤
Senior🎓
Top SchoolJens Wahl is a Senior Data Scientist with nine years of experience applying statistical modelling and scalable engineering to forecasting, risk management, and revenue optimization across insurance, finance, commodities and electricity markets. Currently at Wheelhouse, he focuses on short-term rental forecasting and pricing, building on earlier work at Oliver Wyman where he cut cloud costs by 95% and model runtimes by 90% through a Python library that compresses in-force life insurance data using convex optimization. His background as a research scientist at the Norwegian Computing Center includes spatial risk modelling for water damage, portfolio optimization, and deposit guarantee fund liability modelling, reflecting a strong blend of applied research and production delivery. Jens holds a master’s in actuarial science from the University of Bergen with a thesis on dynamic factor models for evolving asset correlations, and has hands-on pricing experience from Tryg. He combines rigorous probabilistic foundations with practical cloud engineering (Databricks, Azure, Python) and a knack for dimensionality reduction that repeatedly turns complex data into actionable, cost-efficient solutions.
9 years of coding experience
7 years of employment as a software developer
Master, MATHEMATICS AND STATISTICS, Master, MATHEMATICS AND STATISTICS at University of Bergen (UiB)
Stackoverflow
1,434reputation
18kreached
13answers
8questions
Github Skills (37)
shapley10
rcpparmadillo10
rcpp10
xts9
tmb9
machine-learning-models9
r-package9
statistical-inference9
anova9
stochastic9
r-language9
linear-models9
quantmod8
volatility8
tidyverse8
Programming languages (4)
Github contributions (5)
JensWahl/stochvolTMB
Apr 2019 - Aug 2021
Likelihood estimation of stochastic volatility models
Contributions:4 releases, 163 commits, 9 PRs in 2 years 4 months
stochastic-volatility-modelsestimationstochasticlikelihoodvolatility
JensWahl/shapr
Jul 2019 - Jul 2019
Explaining the output of machine learning models with more accurately estimated Shapley values
Contributions:23 pushes, 5 branches in 3 days
shapley-valuesmachine-learningshapleyestimatedmachine-learning-models
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