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Director Data Science at Johnson & Johnson
Lars Hulstaert is a member of J&J's R&D Data Science & Digital Health organisation, within the real-world evidence and advanced algorithms (RWE/AA) group. Lars has over 7 years experience in data science, from hands-on building machine learning pipelines to leading teams of data scientists & data engineers. Much of Lars’ current work is focussed on supporting study design, accelerating clinical trials, improving R&D productivity and leveraging data science to support health equity and diversity in drug development. Before joining J&J, Lars worked as a Data Scientist at Microsoft. In this role, he provided technical consultancy with strategic customers to develop data science solutions. Lars has a master degree in Machine Learning from Cambridge University, where he won a Google Award for his research work in NLP and a Microsoft Award for his teaching. He has also completed a Engineering bachelor & master in Computer Science. Lars is passionate about healthcare, data science & education, enabling others through mentoring.
Ghent University
Master’s Degree, Computer Science
N/A – Present
University of Cambridge
Master of Philosophy (M.Phil.), Advanced Computer Science
N/A – Present
Ghent University
Bachelor's Degree, Computer Science
N/A – Present
Johnson & Johnson Innovative Medicine
Director, Data Science
May 1, 2025 – Present
Johnson & Johnson Innovative Medicine
Associate Director, Data Science
October 1, 2022 – May 1, 2025
Johnson & Johnson Innovative Medicine
Principal Data Scientist
April 1, 2022 – October 1, 2022
Johnson & Johnson Innovative Medicine
Senior Data Scientist
June 1, 2020 – April 1, 2022
Microsoft
Data Scientist
September 1, 2017 – May 1, 2020
Worldwide
DataCamp
Data Science Blogger and Advisory Board Member
June 1, 2017 – November 1, 2019
Antwerp Area, Belgium
Cambridge Spark
Machine Learning Tutor
May 1, 2017 – July 1, 2017
Greater Cambridge Area
Microsoft
Microsoft Student Partner
November 1, 2016 – July 1, 2017
Greater Cambridge Area
IBM
Extreme Blue Technical Intern
June 1, 2015 – September 1, 2015
Brussels Metropolitan Area
Exam 773 : Analyzing Big Data with Microsoft R
Microsoft
June 24, 2026 – Present
Microsoft Professional Program: Data Science and Machine Learning
Microsoft
June 24, 2026 – Present
Microsoft Professional Program: Big Data
Microsoft
June 24, 2026 – Present
Exam 774: Perform Cloud Data Science with Azure Machine Learning
Microsoft
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
MCSE: Data Management and Analytics
Microsoft
June 24, 2026 – Present
MCSA: Machine Learning
Microsoft
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in corporate environments (Johnson & Johnson, Microsoft, IBM) and has also engaged in educational and advisory roles (DataCamp, Cambridge Spark). This blend suggests adaptability and a proactive approach to knowledge sharing and continuous learning. The focus on AI/ML and data science aligns well with innovative, data-driven cultures. The candidate's experience in diverse industries (life sciences, tech) and roles (individual contributor, team lead, director) indicates a broad perspective and ability to integrate into various organizational structures. However, the target role is 'Data Analyst', which is a step down from 'Director, Data Science'. While the skills are highly relevant, the role alignment needs careful consideration regarding career progression and expectations.
Soft Skills & Operational Fit
The candidate's resume highlights leadership, project management, and collaboration skills through descriptions of leading teams, partnering with business stakeholders, and working in cross-functional environments. Experience as a blogger, tutor, and speaker also indicates strong communication and mentoring abilities. The progression through senior roles at Johnson & Johnson demonstrates operational effectiveness and strategic thinking.