Luyolo Magangane
ML EngineerSenior Applied Scientist
HelloFresh
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior Applied 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
Research Interests Knowledge Representation and Reasoning: Open-domain concept modeling using knowledge graphs and statistical relational learning. Graphical Machine Learning: Latent feature and multi-hop graph modeling. Deep Reinforcement Learning: Knowledge-grounded reasoning, optimal decision-making.
Stellenbosch University
Master of Science - MS, Applied Mathematics
January 1, 2018 – January 1, 2020
University of Cape Town
Bachelor of Engineering (B.Eng.), Electrical and Computer
January 1, 2007 – January 1, 2010
HelloFresh
Senior Machine Learning Engineer
July 1, 2024 – Present
Berlin, Germany · Hybrid
Delivery Hero
Senior Machine Learning Engineer
November 1, 2022 – July 1, 2024
Berlin, Germany
Amazon
Applied Scientist
May 1, 2019 – December 1, 2021
Cape Town Area, South Africa
BCX
Senior Data Scientist
January 1, 2018 – April 1, 2019
Cape Town Area, South Africa
takealot.com
Machine Learning Engineer
July 1, 2016 – December 1, 2017
Cape Town Area, South Africa
Digiata
Software Engineer
April 1, 2015 – June 1, 2016
Cape Town Area, South Africa
Cultural Fit Analysis
The candidate has worked in diverse, fast-paced environments (Amazon, Delivery Hero, HelloFresh, takealot.com), indicating adaptability. Their experience in driving best practices and leading chapters suggests a proactive and collaborative mindset. The breadth of projects, from recommendation systems to infrastructure resource management, shows a willingness to tackle varied challenges. However, the lack of explicit project details beyond job descriptions makes a deeper cultural fit assessment challenging.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest strong problem-solving skills, particularly in optimizing algorithms and designing system architectures. Their role as an ML chapter lead indicates leadership and mentorship capabilities. The focus on standardizing infrastructure and developing frameworks points to a structured and process-oriented approach, which is beneficial for operational fit.