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Data Science Manager
Hi there! I'd describe myself as a Data Scientist / Data Engineer with experience from both research and industry, passionate about solving any data-related problems. Genuinely love working closely with people and believe in growing collectively by sharing knowledge and insights, be it to stakeholders, colleagues or just anyone interested to know more!
Uppsala University
Master of Science in Engineering (M.Sc.Eng.), Information Technology
January 1, 2015 – January 1, 2016
Uppsala University
Bachelor of Science (B.Sc.), Technology
January 1, 2012 – January 1, 2015
KTH Royal Institute of Technology
Construction Engineering and Design
January 1, 2009 – January 1, 2010
Spotify
Data Science Manager
April 1, 2020 – Present
Greater Stockholm Metropolitan Area
Volvo Car Mobility
Data Scientist
August 1, 2018 – April 1, 2020
Stockholm, Stockholm County, Sweden
Netlight
Data Science Consultant
November 1, 2017 – May 1, 2021
Stockholm, Sweden
Zettle by PayPal
Data Scientist
November 1, 2017 – August 1, 2018
Stockholm, Sweden
FOI
AI Research Engineer
June 1, 2016 – November 1, 2017
Stockholm, Sweden
Spire AB
Software Engineer
March 1, 2015 – May 1, 2016
Uppsala, Sweden
IKEA
Customer Relations/Sales
January 1, 2008 – January 1, 2014
Static Gesture Recognition using Leap Motion
May 1, 2016 – June 1, 2016
The purpose of this project was to develop an ordering system for a bar where orders were made only by using static hand gestures. With the help of a Leap Motion controller, we used Machine Learning to train a model to recognize 8 different hand gestures, in which the user could (with the support of the UI) navigate the ordering system ordering any number of drinks, foods and even to select payment option solely by using hand gestures. The final system’s model had a 95% accuracy of classifying gestures. Paper available here: https://arxiv.org/abs/1705.05884 Code available here: https://github.com/windmark/static-gesture-recognition
Musical Instrument Recognition System using Artificial Neural Networks
April 1, 2016 – May 1, 2016
In this project, Artificial Neural Networks were trained to classify a range of musical instruments and a number of comparative experiments were conducted on how much different characteristics of the musical instruments would impact the resulting accuracy. The dataset of the experiment was the London Philharmonic Orchestra Dataset, consisting of recorded samples from 20 different musical instruments. The final model had an accuracy ranging between 93.5% - 64.2%, depending on which parts of the audio we trained it with. Paper available here: https://arxiv.org/abs/1705.04971 Code available here: https://github.com/babaktr/musical-instrument-recognition
Cultural Fit Analysis
The candidate has worked in diverse environments, from large corporations (Spotify, Volvo, IKEA) to consulting (Netlight) and research (FOI). This breadth of experience suggests adaptability to different organizational cultures. The personal projects demonstrate initiative and a passion for problem-solving, which aligns with an innovative culture. However, the target role is 'Data Analyst' while the candidate's experience is heavily skewed towards 'Data Scientist' and 'Data Science Manager', which might indicate a potential mismatch in day-to-day responsibilities and expectations for a pure analyst role.
Soft Skills & Operational Fit
The candidate's experience as a Data Science Manager and Data Science Consultant suggests strong leadership, mentoring, and client-facing communication skills. Involvement in agile development (Spire AB) indicates adaptability and teamwork. The project descriptions are clear and well-articulated, suggesting good written communication.