Verified recordSoftware Development

Eva Jaidan

Lead Data Analytics & Ai at Mega International

Based in Paris, France

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Seniority

Manager

Department

Other

Location

Paris

Industry

Software Development

Company size

1K

Contact information

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Email

1 credit

e•••••••@mega.com

Phone

5 credits

+33 ••• •••• ••••

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Background

About Eva Jaidan

I'm Eva, a senior Data Scientist who's all about diving deep into AI and coming up with innovative products and solutions. I've had the privilege of working at leading industrial players such as Vitesco Technologies, Continental, and Airbus Group. Over there, I fine-tuned my skills in crafting cutting-edge AI solutions for product quality improvement and operational cost reduction. My focus has always been centered on sustainable improvements, ensuring lasting benefits for our clients and end users. A pivotal moment in my career was my industrial PhD on generative AI, concentrating on Variational Autoencoders (VAEs) for anomaly detection for the manufacturing industry. This research underscored the importance of AI in the evolving landscape of digital transformation, encompassing digital twin technologies and the nuances of industry 4.0. Throughout my current role, I've realized the immense potential of AI when applied to enterprise architectures. In fact, the world is evolving rapidly, and for businesses to stay ahead, agility is key. Enterprise architecture plays a pivotal role in this, yet many companies find the journey long and intricate. My aim? To accelerate this process by leveraging AI, in collaboration with leading research centers and mathematical institutes. Currently, as a Lead Data Scientist - head of IA and Analytics at MEGA International, my focus is on driving innovation in AI and translating its prowess to empower business growth and enhance user experience. I believe in explainable and acceptable AI, a philosophy I've consistently championed, even during my tenure at Vitesco Technologies where I collaborated with domain experts, supervised industrial PhD students, and co-organized data science training seminars.

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