Datacast

Jannes is a data scientist at QuantumBlack and author of the book "Machine Learning For Finance". He previously studied financial economics at Oxford University where he wrote his thesis on the predictability of banking stress tests.

Jan Zawadzki has 6 years of experience at a global consultancy and as a Data Scientist. Jan currently works in the realm of self-driving cars as a Project Lead Data Science for Carmeq GmbH, the innovation vehicle of Volkswagen AG. Jan is passionate about advancing the automotive industry through machine learning and sharing his knowledge in the fields of Business and Data Science. He is a top contributor to the “Towards Data Science” Publication on Medium. He is also a deeplearning.ai Ambassador, supporting the team around Deep Learning Luminary Andrew Ng.

Admond is currently a Data Scientist at Micron focusing on Smart Manufacturing and AI. With his degree in Physics, Admond discovered and pursued his passion in data science and never looked back ever since. He is also an independent data scientist and consultant helping companies and digital marketing agencies achieve their marketing ROI using advanced social analytics and machine learning. Being a passionate writer who writes about data science, his data science work and experience have been featured by various publications, including KDnuggets, Medium, Tech in Asia, AI Time Journal and local business magazine.

Sunanda Koduvayur Parthasarathy is an Associate Director of Data Science at Wayfair Inc., where she leads a team of data scientists and engineers to build machine learning solutions that lead to a better shopping experience for all Wayfair customers. Prior to this, she was leading data science innovation R&D projects at the ad-tech startup DataXu. Through her career in data science, she has enjoyed bringing quantitative definition and clarity to unwieldy strategic business questions that have led to multiple high-revenue products.

Tristan Bergh is a systems thinker and data scientist living in Cape Town, South Africa. He studied aeronautical engineering before working as an enterprise software engineer. He worked as architect on AFIS implementations in Southern Africa, moving into middleware for large fintech systems. He restarted his system modeling and machine learning consulting services through 2014, delivering predictive analytics models in healthcare and other domains. Tristan’s skills are that of a full-stack data scientist, using primarily PySpark to deliver predictive analytics models in a rules-engine environment. He backs up his machine learning at scale using R and Python to find optimal models and feature sets for use in modeling. His current area of interest is setting up customer next-best-action processes at scale.

Ankit Gupta is a data scientist at Crisis Text Line, a New York-based not-for-profit tech startup providing 24/7 free text-based crisis support to individuals. At Crisis Text Line, Ankit developed an AI-driven triage system that detects signals of suicidality within the first few messages sent by texters. Using the triage system, the counselors can serve 93% of at-risk texters in under five minutes.

Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her PhD from Brown University in 2016 under the direction of James Hays.

Peadar Coyle is a Data Scientist and Engineer based in London. He regularly speaks at conferences and has written a book consisting of numerous interviews with Data Scientists throughout the world. He is also a passionate Open-Source evangelist, himself a supporter who has contributed to PyMC3. Most recently, he founded a stealth startup that is working on hyper-personalised audio.

Nick Gaylord has worked as a data scientist in the Bay Area for about the last 5 years. Currently, he’s a member of the Johnson & Johnson Health Technology team, and prior to that, he has worked in different fields ranging from small business revenue analytics to enterprise machine-learning-as-a-service platforms. Like many data scientists, he started out as an academic before transitioning to industry, in his case earning a Ph.D. in Psycholinguistics from the University of Texas at Austin in 2013.

Conor Dewey is a data scientist at Squarespace who spends his time thinking about growth and engagement. He frequently shares insights from his experience interviewing at top companies. More generally, he offers resources and advice on Medium and Github in the hopes of 'open sourcing' his journey to data science. Conor also manages a weekly data science newsletter with 1000+ subscribers.