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machine learning, computer vision
Current role: deep learning engineer, computer vision Main expertise: 2d and 3d computer vision, pose estimation, object detection and model deployment on embedded devices. Only interested in positions like: machine learning engineer, data scientist, algorithm developer Technology areas I'm exited about: algorithm development, artificial intelligence and machine learning: in particular deep learning, computer vision, big data, signal processing, image processing, audio and mathematically oriented programming in general. Would love to do a PhD! Programming languages: Python, Haskell, Scala, C, C++, Java, R, Matlab/Octave. Most experienced in: Python, C/C++ Data science and machine learning: Tensorflow/Keras, Pandas, Scikit-learn, Numpy, Open CV, Eigen Big data: Apache Spark. Version handling: Git, Clearcase, SVN. Testing frameworks: JUnit, Android Test, Powermock, Easymock, Google Test. Platforms: Android, OSE, Linux, Windows, Blackberry 10, OSX.
The Faculty of Engineering at Lund University
Master's Degree, Electrical, Electronics and Communications Engineering
January 1, 2003 – January 1, 2008
Lund University
Musicology and Ethnomusicology
January 1, 2002 – January 1, 2003
Verisure
Senior Machine Learning Engineer
December 1, 2024 – Present
Crunchfish AB
Senior Research Engineer
May 1, 2022 – December 1, 2024
Malmö, Skåne County, Sweden
Axis Communications
Machine Learning Engineer
June 1, 2017 – May 1, 2022
Lund, Sweden
Lytics
Data Scientist
April 1, 2015 – August 1, 2016
Malmo, Sweden
BlackBerry
Software Developer
August 1, 2012 – February 1, 2014
Malmö, Sweden
ST-Ericsson
WCDMA Layer 1 Developer, Modems Division
August 1, 2011 – July 1, 2012
Lund, Sweden
Sony Ericsson
Developer, DRM
September 1, 2009 – July 1, 2011
System Verification
R&D Engineer
March 1, 2008 – August 1, 2012
Malmö, Sweden
ST-Ericsson
Simulation Engineer, WCDMA Physical Layer Simulation
March 1, 2008 – December 1, 2008
Ericsson
Master thesis student, Ericsson Research
September 1, 2007 – February 1, 2008
Cultural Fit Analysis
The candidate has a strong background in engineering and research, with a clear progression through roles focused on machine learning, computer vision, and data science. The transition from embedded systems and telecommunications to data science and machine learning demonstrates adaptability and a continuous learning mindset. The diverse project experience, from medical data analysis to hand tracking and surveillance, suggests an openness to different problem domains. However, the target role is 'Data Analyst', while the candidate's recent experience is heavily skewed towards 'Machine Learning Engineer' and 'Research Engineer'. This might indicate a potential mismatch if the Data Analyst role is purely focused on reporting and basic analytics rather than advanced modeling and research. The candidate's experience level (20 years) is very high for a typical Data Analyst role, which could lead to overqualification or a desire for more advanced responsibilities.
Soft Skills & Operational Fit
The candidate's resume highlights roles in research and development, suggesting a problem-solving mindset and an ability to work on complex, novel challenges. Experience in various companies (Verisure, Crunchfish, Axis, Lytics, BlackBerry, Sony Ericsson) indicates adaptability and exposure to different operational environments. The descriptions imply a focus on technical execution and optimization, which aligns with a results-oriented operational fit. However, specific soft skills like teamwork, leadership, or communication are not explicitly detailed in the provided descriptions.