Morgan Pihl
Software Developer at Polismyndigheten
Sweden
Summary
👤
Senior🎓
Top SchoolMorgan Pihl is a software developer and applied machine learning specialist with 11 years of experience building statistical models and production ML systems, currently contributing to public-sector technology in Sweden. He has led R&D and people-science teams at Alva Labs, shipping large-scale parameter estimation and real-time inference pipelines on Google Cloud while designing transparent decision-support algorithms for talent assessment. At Klarna he built credit risk infrastructure and cross-functional data products, and his open-source contributions to the widely used PyMC probabilistic programming project include implementing moment calculations and tests for complex distributions. Combining formal training in psychology with deep quantitative and engineering skills, Morgan excels at translating psychometric insight into robust, auditable ML solutions.
11 years of coding experience
4 years of employment as a software developer
Upper secondary school Natural Science program, Upper secondary school Natural Science program at Katedralskolan i LinköpingMaster of science Psychology, Master of science Psychology at Umeå University
French, English, Swedish
Github Skills (11)
statistics10
mcmc10
bayesian10
probabilistic-programming10
python10
bayesian-inference10
statistic10
testing10
pytest9
variational-inference8
numpy8
Programming languages (1)
Github contributions (5)
pymc-devs/pymc
Nov 2021 - Dec 2022
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist / QA Engineer
Data Scientist / QA EngineerContributions:9 reviews, 5 commits, 7 PRs in 1 year 1 month
Contributions summary:Morgan primarily contributed to the PyMC project by adding and modifying tests, specifically for the moment calculations of various probability distributions. They implemented test cases for beta-binomial, inverse gamma, multinomial, and Dirichlet multinomial distributions. Additionally, the user also contributed to the underlying distribution code by adding support for moment calculation, showcasing their understanding of the mathematical and statistical concepts involved in probabilistic programming.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
morganstrom/utility-calculator
Jun 2016 - Apr 2020
Contributions:17 commits, 13 pushes, 1 branch in 3 years 10 months
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