Jack Baker
Data ScientistData Scientist
ManchesterMember since June 26, 2026
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Scientist
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
ds-kubernetes-templates
November 20, 2022 – November 20, 2022
Kubernetes templates, tutorials and patterns for deploying data science jobs
View Projectcpp-algorithms
November 5, 2022 – November 18, 2022
Learning C++ by implementing various algorithms
View ProjectSCIR
July 20, 2018 – July 20, 2018
Companion code to the NeurIPS publication Baker, Fearnhead, Fox, Nemeth (2018) Large-Scale Stochastic Sampling from the Probability Simplex. Available from https://papers.nips.cc/paper/2018/hash/900c563bfd2c48c16701acca83ad858a-Abstract.html
View Projectcontrol-variates-sgld
February 2, 2018 – July 19, 2018
Companion code to the publication Baker, Fearnhead, Fox, Nemeth (2018) Control Variates for Stochastic Gradient MCMC. Statistics and Computing. Available from https://link.springer.com/article/10.1007/s11222-018-9826-2.
View ProjectBigDataMCMCComparisons
February 2, 2018 – February 5, 2018
Code which compares various algorithms for MCMC on large datasets. See e.g. https://arxiv.org/abs/1706.05439v1 or http://lancs.ac.uk/~bakerj1/pdfs/unpublished/bigDataMCMCComparison.pdf for the results.
View Projectsgmcmc-data
September 25, 2017 – September 25, 2017
Datasets for vignettes in sgmcmc package
View Projectsgmcmc-simulations
September 22, 2017 – April 13, 2018
Example simulations using the sgmcmc package for R
View Projectsgmcmc
March 21, 2017 – November 5, 2020
sgmcmc: a stochastic gradient MCMC package for R
View Projectmatrix_factorization
December 23, 2016 – January 2, 2017
Simple matrix factorization algorithm for a recommender system trained using stochastic gradient descent
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on academic research and personal exploration of algorithms, particularly in statistical computing and machine learning. While this demonstrates deep technical interest, the lack of team-based or industry-oriented projects makes it difficult to assess cultural fit for a typical corporate data scientist role. The projects are diverse in terms of algorithms and languages, but narrow in application domain.
Soft Skills & Operational Fit
The provided data does not contain information to assess soft skills or operational fit. The candidate's project descriptions suggest a strong research and implementation focus, often tied to academic work.