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Building/Scaling Your Data Science Team: Best Practices Panel

Photo of
Hosted By
Aaron M. and 2 others


A panel discussion on best practices for growing a Data Science team. Panelists will focus on keys to success at their companies as well as respond to a prospective case study. Pizza and drinks generously provided by PagerDuty.

Panel Discussion Topics: How can a company know if it is ready for data science? How might they put the right team together? What else should they know before starting or expect along the road? Panel questions will be provided ahead of time, and will cover panelists' past experiences, perceptions of best practices, and questions relevant to the case study.

The Meetup will enable both participants and attendees to learn from each other's experiences on what is required to create and maintain a successful Data Science team.

Current Panelists:

• Raaid Ahmad from Weebly

• Parvez Ahammad from Instart Logic

• Eric Colson from StitchFix (formerly VP of DS at Netflix)

• Zach Kagin from Dropbox

• Jeremy Schiff from OpenTable

Panelist Bios:

Raaid is the head of analytics and data science at Weebly where he is focused on scaling his team and creating best practices for the new function. Before Weebly he spent 3 years at Kiwi, a mobile gaming company where he oversaw the centralized BI function, quantitative game economy development, and built ensemble learning models to target users based on behavioral patterns. Earlier in his career he enjoyed 5 years at Bridgewater Associates, the world's biggest hedge fund, where he led the high risk trades team and developed proprietary trading algorithms for new markets and exceptionally large trades. Prior to his time in an office, Raaid played poker professionally from 2005-2007 and developed a unique "barbell" play strategy as a countermeasure for poker tracking software, nearly doubling his win-rate against opponents using the software. Raaid holds an MBA from Stanford's Graduate School of Business and a BS in Computer Science, Applied Mathematics & Statistics from Johns Hopkins University's School of Engineering.

Parvez Ahammad currently leads the data science and machine learning group at Instart Logic Inc. He earned his PhD in computer vision and machine learning from UC-Berkeley. He has 15+ years of experience in computer vision (CV), machine learning (ML), statistics and signal processing. His work spans diverse application domains such as web application delivery, camera sensor networks, bioinformatics and neuroscience. He is the creator of novel algorithmic technologies such as smartVision (patent pending) @ Instart Logic, OpSIN and Salient Watershed @ HHMI-Janelia, to name a few.

Eric Colson is the Chief Algorithms Officer at Stitch Fix, where he specializes in social algorithms. He is also an advisor at several companies: Earnest Inc (consumer lending), Data Elite (Big Data incubator), Mortar Data (Big Data Platform). Previously, he was VP of Data Science & Engineering at Netflix and has held analytics positions at Yahoo!, Blue Martini, Proxicom and Information Resources. He holds a B.A. in Economics, a M.S. in Information Systems, and a M.S. in Management Science & Engineering.

Zach Kagin is the Head of Data Science and Product Analytics at Dropbox. Prior to that, he was a product manager driving growth efforts for Dropbox for Business and Dropbox's mobile apps. Before joining Dropbox, Zach worked at The Boston Consulting Group as a management consultant focused on tech and product strategy. Zach has a BS in Physics and Economics from Yale University, and in his free time, he likes to write comics and build snow forts.

Jeremy Schiff earned an undergraduate in Electrical Engineering and Computer Science from the University of California in 2005, and a Ph.D. in Electrical Engineering and Computer Science in 2009, with a focus on applying machine learning and statistical inference to robotics. In 2006, Jeremy co-founded, an online photo editing company that powered companies such as MySpace and Photobucket. In 2009, Jeremy joined Ness Computing, a Personalized Search and Recommendation company. As VP of Machine Learning, he oversaw the efforts around Personalized Recommendations, and other data-driven features. Ness sold to OpenTable in 2014, where Jeremy now leads Data Science.

Case Study:

PagerDuty (PD) provides alerting, on-call scheduling, escalation policies and incident tracking to increase uptime on customer apps, servers, website and databases. As the focal point between monitoring tools and the people resolving system incidents, PD maintains an extensive dataset with the potential to optimize the on-call process. PD intends to grow out a Data Science team that will enable it to trace seemingly unrelated incidents back to a single real-world cause, find patterns in how incidents are triggered, and help PD differentiate the optimal customer experience by learning from customers that churn.

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