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Lecturer in Machine Learning at University of Aberdeen, UK
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Assessing your cultural and operational fit
University of Aberdeen
Data Scientist
June 19, 2026 – Present
seq2point-nilm
June 28, 2020 – June 28, 2020
Sequence-to-point learning for non-intrusive load monitoring
View ProjectMingjunZhong.github.io
November 10, 2019 – May 30, 2020
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
View ProjecttransferNILM
August 23, 2019 – July 5, 2020
Transfer Learning for Non-Intrusive Load Monitoring
View ProjectNeuralNetNilm
December 7, 2017 – July 2, 2020
Sequence-to-point learning for non-intrusive load monitoring (energy disaggregation)
View ProjectLatentBayesianMelding
November 19, 2015 – May 8, 2019
Latent Bayesian melding for non-intrusive load monitoring (energy disaggregation)
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily concentrated on Non-Intrusive Load Monitoring (NILM) and related machine learning applications, indicating a deep but narrow technical focus. While this specialization is valuable, the lack of diversity in project domains and technologies (predominantly Python) suggests a potentially limited breadth of experience for a senior Data Scientist role that often requires adaptability across various problem spaces. The single listed professional experience as 'Data Scientist' at a university, with a future start date, provides insufficient information to assess real-world industry collaboration or broader team integration capabilities. The candidate's profile aligns well with research-heavy data science roles but may require further validation for roles demanding diverse industry applications.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's experience is primarily academic and personal project-focused, with no completed psychometric or English tests.