Kaito Suzuki
Salford, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolKaito Suzuki is a CRM and digital marketing specialist with eight years’ experience across SaaS and financial services, skilled at turning data into automated customer journeys that boost leads and reduce manual work. At Valuedesign he drove a 120% increase in lead generation and implemented Pardot scoring to shift focus to lead quality, while earlier roles at NEO CAREER cut support man-hours by 25% through AI chatbots and TechTouch flows. He combines hands-on CRM engineering (Salesforce, Pardot, Google Apps Script) with customer success strategy to improve conversion and retention. Uncommonly for a marketer, he contributes to machine-learning tooling—working on Optuna’s core trial and distribution handling—bringing a technical edge to optimisation and experimentation. Based in Salford and bilingual in English and Japanese, he is open to CRM, customer success, or digital marketing opportunities in the UK.
8 years of coding experience
Bachelor of Business Administration - BBA, Business Administration and Management, General, Bachelor of Business Administration - BBA, Business Administration and Management, General at Seijo University
Japanese, English
Github Skills (6)
hyperparameter-optimization10
machine-learning10
python10
testing10
back-end-development9
cli8
Programming languages (6)
Github contributions (5)
optuna/optuna
Nov 2022 - Dec 2022
A hyperparameter optimization framework
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:125 reviews, 28 commits, 42 PRs in 1 month
Contributions summary:Kaito made several updates to the Optuna library, primarily focused on improving the functionality of parameter distributions and trial handling within the hyperparameter optimization framework. Their contributions included modifying the command-line interface (CLI) and updating testing procedures for various parameter distributions. Furthermore, the user implemented changes to the core trial and study management aspects of the library, including enhancements to the internal representation of parameter values and system attributes. These changes suggest a focus on improving the library's core machine-learning capabilities.
pythonoptimization-frameworkparallelhyperparameteroptimization
cross32768/PlaNet_PyTorch
May 2020 - Jun 2020
Contributions:80 commits, 38 pushes in 22 days
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