DR. Anat Eck

Shape up and take a leap of faith: Tips for a successful transition from academia to data science in industry

DR. Anat Eck

DR. Anat Eck

Shape up and take a leap of faith: Tips for a successful transition from academia to data science in industry

DR. Anat Eck

Bio

I am a researcher turned into a product-focused data scientist. I hold a Ph.D. in bioinformatics. After years of exploring the human gut microbiota, I decided to pivot my career towards data science in the industry, aiming to develop machine learning models that bring real business value.

Bio

I am a researcher turned into a product-focused data scientist. I hold a Ph.D. in bioinformatics. After years of exploring the human gut microbiota, I decided to pivot my career towards data science in the industry, aiming to develop machine learning models that bring real business value.

Abstract

Advanced degrees (MA or Ph.D.) are held by 75% of data scientists, and data scientists have more PhDs than any of the other job titles in related fields. These facts indicate that what I considered my own personal and specific career journey is quite common. It also means that many other fresh academic graduates face the same challenges I faced when looking to kick off my career in the industry. After 10+ years in academia, and even though I carried out my Ph.D. at a small biotech startup, I was deeply puzzled by the confusing job titles, requirements, and lack of appreciation for my academic experience. Different work methodologies, work environments, and even the core way of thinking and approaching problems may sometimes seem like a barrier too high to cross as a researcher. However, by understanding the industry mindset, how it differs from the academic one, and the expected gaps, you, as a researcher, can successfully land the job you are aiming for and succeed in it. In this roundtable, we will discuss the challenges and provide practical tips on how to overcome them.

Abstract

Advanced degrees (MA or Ph.D.) are held by 75% of data scientists, and data scientists have more PhDs than any of the other job titles in related fields. These facts indicate that what I considered my own personal and specific career journey is quite common. It also means that many other fresh academic graduates face the same challenges I faced when looking to kick off my career in the industry. After 10+ years in academia, and even though I carried out my Ph.D. at a small biotech startup, I was deeply puzzled by the confusing job titles, requirements, and lack of appreciation for my academic experience. Different work methodologies, work environments, and even the core way of thinking and approaching problems may sometimes seem like a barrier too high to cross as a researcher. However, by understanding the industry mindset, how it differs from the academic one, and the expected gaps, you, as a researcher, can successfully land the job you are aiming for and succeed in it. In this roundtable, we will discuss the challenges and provide practical tips on how to overcome them.

Planned Agenda

8:45 Reception
9:30 Opening words by WiDS TLV ambassadors Or Basson and Noah Eyal Altman
9:40 Dr. Kira Radinski - Learning to predict the future of healthcare
10:10 Prof. Yonina Eldar - Model-Based Deep Learning: Applications to Imaging and Communications
10:40 Break
10:50 Lightning talks
12:20 Lunch & Poster session
13:20 Roundtable session & Poster session
14:05 Roundtable closure
14:20 Break
14:30 Dr. Anna Levant - 3D Metrology: Seeing the Unseen
15:00 Aviv Ben-Arie - Counterfactual Explanations: The Future of Explainable AI?
15:30 Closing remarks
15:40 End