Einar Magnusson
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Applied AI ML Director @ JPMorganChase | PhD in Quantum Physics
I am an Applied AI ML Director at JPMorganChase, with 8 years of experience in applying machine learning and artificial intelligence to solve complex problems in financial services, I have a PhD in Quantum Physics from the University of Oxford, where I also received a prestigious Clarendon Scholarship. I am passionate about keeping up with cutting-edge developments in AI, high-performance computing, and quantum computing. I have collaborated with academic institutions and industry partners on quantum machine learning and artificial intelligence. My mission is to empower and enable my team and my organization to be at its best using AI and other advanced technology.
University of Oxford
Doctor of Philosophy (Ph.D.), Physics
January 1, 2012 – January 1, 2016
ICFO, Institute of Photonic Sciences
Summer fellow, Nanophotonics
January 1, 2011 – Present
Háskóli Íslands
Master of Science (M.Sc.), Theoretical and Mathematical Physics
January 1, 2010 – January 1, 2012
Harvey Mudd College
Engineering
January 1, 2009 – Present
Háskóli Íslands
Bachelor of Science (B.Sc.), Physics
January 1, 2006 – January 1, 2009
JPMorganChase
Applied AI ML Director
September 1, 2024 – Present
London, United Kingdom · Hybrid
HSBC
Head of Applied AI, Markets and Securities Services
September 1, 2023 – September 1, 2024
HSBC
Head of AI Capabilities, Markets & Securities Services
February 1, 2023 – September 1, 2023
HSBC
Senior Data Scientist, Markets and Securites Services
January 1, 2020 – February 1, 2023
HSBC
Data Scientist, Corporate and Institutional Digital
January 1, 2018 – January 1, 2020
Innovation Center Iceland
Machine learning researcher
May 1, 2017 – December 1, 2017
Reykjavik, Iceland
Science institute, University of Iceland
Researcher
May 1, 2012 – August 1, 2012
Reykjavik, Iceland
ICFO
Summer fellow
June 1, 2011 – September 1, 2011
Greater Barcelona Metropolitan Area
Reykjavik Energy
Intern
May 1, 2008 – August 1, 2008
Reykjavik, Iceland
Science To Data Science
August 1, 2017 – September 1, 2017
S2DS is a highly competitive data science course where participants work on real-life problems with startups and established companies. During my time at S2DS I worked with a healthcare startup, biotx.ai. They are disrupting the drug development industry by applying modern machine learning techniques to genomic disease association. This type of work involves dealing with genome data that has millions of variables but typically only a few thousand rows. Our project involved analysing data for a particular disease that affects about 0.5% of the population: - Prepared/cleaned data from academic studies, developed a workflow. - Applied advanced feature selection algorithm and developed predictive models for disease outcome. - Generated decision trees to visualise new genetic pathways to disease. - Identified potential new high risk group, paving the way for a future clinical study on personalised medication based on the patient’s genome.
Scitocols: collaboration platform for researchers
June 1, 2015 – August 1, 2016
Out of frustration in our research and passion for entrepreneurship, we decided to create a web platform for scientists to collaborate on and share their research protocols. We got £16000 in funding from the University of Oxford IT Department to work on this project.
SocialSolar
June 1, 2014 – May 1, 2015
A charity with the aim to provide solar power for communities in rural India with the help of crowdfunding. We pitched our idea successfully to Climate-KIC and received financial support as well as business mentoring. Installed a 2.6 kW panel on a school of 350 students.
S2DS London 2017
Pivigo
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an ML Engineer role, especially in an innovative and challenging environment. Their progression from a research background to leading AI teams in finance shows adaptability and a drive for practical application. Involvement in projects like 'Science To Data Science' and 'SocialSolar' highlights a commitment to impactful work and a diverse problem-solving mindset. The leadership roles in building AI capabilities and setting platform strategy align well with a senior, influential position. The quantum computing research also indicates a forward-thinking and continuous learning mindset.
Soft Skills & Operational Fit
The candidate's experience in leading teams, engaging with stakeholders (front office, ops, IT), and managing communication in interdisciplinary projects (e.g., Science institute, Scitocols) suggests strong communication, collaboration, and leadership skills. Their involvement in entrepreneurial projects (Scitocols, SocialSolar) indicates initiative and problem-solving abilities. The description of tackling 'massive scale' challenges and 'complex resolution workflows' implies resilience and operational fit for demanding environments.