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Thank you for your interest in this Dataiku Washington D.C. Meetup! The health & safety of our attendees and speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content and facilitating discussions in the data science space. As such, we’ve decided to pivot towards online webinars via our partner platform, Brighttalk. The entire Meetup in the same format will be held virtually, which allows for a live presentation and Q&A session after. IMPORTANT: In order to gain entry, you must RSVP through this BrightTalk link: https://www.brighttalk.com/webcast/17108/411600?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=411600 Tentative Schedule: 2:00pm: Intro 2:05pm: Strategy and Marketing Attribution Models with Squarespace by Braden Purcell, Omar Abboud, and Nate Lawson from Squarespace 2:45pm: Q&A Talk Abstract: Squarespace’s Data Science team helps the company make better strategic decisions using data. The Data Science and Marketing teams collaborate closely to ensure that they quantify the impact of their marketing decisions and use data to optimize spend allocation. In the fireside chat, they will be asked to give a broad overview of marketing data science at Squarespace. They will also be asked to provide examples as to how they have improved the quality of their checkout survey that serves as a key input to their marketing attribution model. To wrap up, they will hang out for a Q&A session with members of both data science and marketing teams to answer questions about how they work together. Speaker bios: Braden Purcell is a data scientist on the Marketing Data Science team at Squarespace. He helps the team develop tools and data products to optimize marketing spend and forecast performance. Before that he was a postdoctoral scientist at the NYU Center for Neural Science where he used experiments and computational modeling to understand how the brain makes decisions. He has a PhD from Vanderbilt University studying cognitive neuroscience. Omar Abboud is a data scientist on the Marketing Data Science team at Squarespace. Over the course of his time with the company, he has worked with various media teams to design experiments, develop measurement tools, and bring clarity to the Squarespace acquisition funnel through the lens of data science. Omar also works closely with Squarespace’s International Strategy team, using statistics and data analysis to inform the company’s strategic direction for international expansion. He holds an M.S. in Computational Science from Harvard University. Nate Lawson is a Data and Operations Lead within the Media & Acquisition team at Squarespace. He provides strategy, guidance, and oversight to marketing channel leads as to how best to implement, track, and report on media initiatives. He also works closely with the Data Science team in developing these strategies. Prior to this role, Nate managed Paid Social performance at The New York Times and General Assembly. He attended Purdue University in Indiana with a focus on technical writing.
Thank you for your interest this Dataiku Washington D.C. Meetup! The health & safety of our attendees and speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content and facilitating discussions in the data science space. As such, we’ve decided to pivot towards online webinars via our partner platform, Brighttalk. The entire Meetup in the same format will be held virtually, which allows for a live presentation and Q&A session after. IMPORTANT: In order to gain entry, you must RSVP through this BrightTalk link: https://www.brighttalk.com/webcast/17108/412085?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=412085 Tentative Schedule: 2:00pm: Intro 2:05pm: Reducing AI Bias and Optimizing Data Labeling Frameworks w/ Appen) by Monchu Chen 2:45pm: Q&A Talk Abstract: Bias in machine learning has become a significant concern as AI technology spreads to more application domains. While some bias is a consequence of limits in design and tooling, bias in the training data itself is much more common. Skewed training data often promotes AI models that reveal discrimination and amplify human prejudices. In this talk, we present a framework, developed at Appen, to minimize bias. This framework operates by routing data labeling tasks to the right labelers to avoid introducing bias. It also optimizes the process by determining a proper distribution of labelers for a given task. Our speaker, Monchu Chen, will review some use cases where this framework has been applied, and discuss results that show how the optimization process minimizes skew in the training data. Chen will also discuss extending this approach to other use cases and review the implications of this work. Speaker bios: Monchu Chen has worked in human-computer interaction for more than two decades. He has helped corporations and startups apply user insights to product innovation in multiple application domains. Monchu now focuses on the human aspects of AI as the Principal Data Scientist for Appen's ML team, building models and systems to improve annotation quality, efficiency, and reducing AI bias. Dr. Chen holds a PhD from Carnegie Mellon University. He previously held a tenured faculty position at Carnegie Mellon Portugal and is the author of more than 70 publications and patents.
Thanks for your interest this Dataiku Washington D.C. Meetup! The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, we’ve decided to pivot towards online webinars via our partner platform, Brighttalk. IMPORTANT: In order to gain entry, you must RSVP HERE: https://www.brighttalk.com/webcast/17108/413296?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=413296 Tentative Schedule: 2:00pm: Intro 2:05pm: Data Science Disruption Across Industries ft. Nordstrom, Hulu, & Atomwise 2:45pm: Q&A Talk Abstract: The impact of data science has been felt across a range of industries including retail, pharmaceuticals, & media. As the retail sector strives to stay technologically significant, data science has emerged as a lifeguard that can be leveraged to predict customer attitudes, visualize customer behavior, & implement knowledgeable decisions. In this fireside chat, we will cover how investments in data science by behalf of the retail industry has heightened the capabilities of retailers beyond just data collection & analysis. The pharma industry has also emerged as an industry where data science is increasing its application. We will touch on applications of data science within pharmaceuticals ranging from identifying suitable candidates for trails based on their physiological chemical structure, medical history, & more. The media & entertainment industry have also ventured into a digitally driven space & the amount of customer data available has exponentially increased. The bulk of the data work today within media & entertainment is dedicated to audience understanding & answering questions such “What are people reading, listening to, & watching?” We will discuss how modern applications of data science in the media & entertainment industry establish new rules & demand extra creative thinking from industry holders. Speaker bios: Skander Hannachi is a data scientist working for Nordstrom's demand forecasting & replenishment team. His current interests are time series models & optimization algorithms applied to merchandising & supply chain, & the use of NLP in business analytics. Prior to that, he worked in consulting roles, implementing retail forecasting & planning solutions for various retailers. He holds a Ph.D in computational intelligence & has done academic research on neural networks & fuzzy logic, both from a theoretical point of view, as well as applied to fisheries & environmental sciences. He also occasionally blogs about Data Science related topics on Medium, which you can read at medium.com/@skanderhannachi . Jared Thompson, PhD is a data scientist & software engineer with over a decade of experience in the pharmaceutical space. He is currently working with the wonderful team at Atomwise, where his primary efforts are towards the prediction of the biological activity of a drug molecule from its X-ray crystal structure. He has professional interests in control theory & optimization, information theory, agent-based models & intelligent systems, particularly interpretable & self-teaching neural architectures. Moe Lotfy is a Senior Product Data Scientist at Hulu leading the Subscriber Acquisition Data Analytics & Experimentation function where he combines statistical techniques with predictive modeling to uncover business insights & inform strategic product decisions. Prior to Hulu, he worked at KPMG where he led efforts to build marketing-focused ML models for several clients, as well as, at Tesla where he built predictive models to guide autopilot hardware’s thermal control strategy. Moe received his PhD in Mechanical Engineering at UCLA with a focus in nuclear fusion experimentation & numerical analysis. His passions include the state of AI, VR, shipping high-impact products, & all things data/experimentation.
Thanks for your interest in this Dataiku Washington D.C. Meetup! The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, we’ve decided to pivot towards online webinars via our partner platform, Brighttalk. IMPORTANT - RSVP HERE: https://www.brighttalk.com/webcast/17108/414807?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=414807 Tentative Schedule: 7:00pm: Intro 7:05pm: Big Data & AI in Finance ft. RBC, ATB Financial, & Scotiabank 7:45pm: Q&A Talk Abstract: AI is utilized by financial institutions in various ways to improve their operations. Its diverse applications affect both the sell side which include investment banking & stockbrokers, as well as the buy side, which include asset managers & hedge funds. In this talk, we’ll dive into how various financial services leverage machine learning, alternative data sets, & other advanced analytics to tackle issues such as credit decisions, risk management, fraud prevention, trading, personalized banking, process automation, & more. We’ll touch on use cases, the impact of big data, & the benefits of that big data & AI can bring to financial services. Speaker bios: Zain is working as a Sr. Data Scientist at the Customer Insights, Data & Analytics group at Scotiabank. He uses data to help the bank meet its strategic ‘customer-first’ objective. Zain leverages machine learning & other advanced analytics to enhance Scotiabank’s knowledge about its customers & deliver a better customer experience. He has a PhD in Soft Computing & worked in academia before starting the current role. Dhruv is a data scientist on ATB Financial’s AI R&D team. He studied machine learning for financial engineering at the University of Toronto & started his career in data science at Sysomos, a social media analytics company where he focused on NLP for sentiment analysis on Twitter data. After graduating, he worked as a management consultant at Deloitte, helping Canadian companies automate their accounting processes. Dhruv also developed a model & app for predicting the replaceability of jobs to automation & the gig economy. At ATB, his team attempts to push the boundary of how machine learning can be used in the financial industry. Fouad has over 12 years of experience in data science & educational outreach. He currently works as a Data Scientist at RBC, developing ML models that predict risk & provide business insights. Previously, he worked as a data scientist at The Ontario Institute for Cancer Research, where he was part of many computational developments that aimed to improve cancer prognosis. In addition to instructing a statistics course at Seneca College & Data courses at BrainStation, he has taught a variety of workshops in Toronto, NY, Montreal & São Paulo that helped scientists learn programming & data analysis skills. In 2018, Fouad founded The Coding Hive, a Toronto based training program that offers high school students & professionals a series of AI workshops.