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Mike Yung
Data Science @ Google
Professional Background
Mike Yung is a seasoned data scientist with more than nine years of extensive experience in product analytics, machine learning, and causal inference. His impressive career trajectory is characterized by his deep commitment to leveraging data science and machine learning techniques to make impactful business decisions.
Currently, Mike is instrumental in building product analytics from the ground up at Google Shopping, focusing on innovative areas like Generative AI and Augmented Reality (AR) products. His role is critical in enhancing the decision-making capabilities of the organization and ensuring that data-driven insights lead to actionable business strategies.
Before joining Google, Mike demonstrated his expertise as a Senior Machine Learning Engineer at Spotify, where he was pivotal in creating predictive models to estimate the value of original podcast content. This innovative work not only solidified Spotify's position in the competitive podcast landscape but also underscored the importance of data in content strategy and user engagement.
Mike's data science journey began at Uber, where he co-authored the open-source package CausalML. This influential project has had a significant impact on performance marketing algorithms within Uber's global marketing operations, which involve multi-billion dollar budgets. Mike's work here reflects his expertise in causal inference and his ability to develop tools that enhance marketing efficiency through data-driven insights.
In addition to his time at Uber, Mike has held various roles that enriched his understanding of data science across different industries. He was a Forward Deployed Data Scientist at Retina AI, where he applied machine learning models to healthcare data and provided insights that drove operational improvements in healthcare management. At Pluto TV, Mike worked as a Data Scientist, focusing on optimizing content strategies and enhancing viewer engagement through advanced data analytics. His early career included a role as an Analyst at PSB Insights, where he honed his analytical skills and learned the nuances of data interpretation.
Education and Achievements
Mike Yung's academic foundation is solid, having studied Economics at the prestigious University of Pennsylvania. This education provided him with a strong analytical framework and an understanding of economic principles that he applies in his data science career. Additionally, he completed a Data Science Fellowship at Galvanize Inc, where he deepened his technical skills and prepared himself for the challenges in the data science field.
As a thought leader within the data science community, Mike has actively contributed to open-source projects and shared his expertise through various forums. His work with the CausalML package demonstrates not only his technical capabilities but also his commitment to fostering collaboration and innovation in the field of data science. His contributions have set benchmarks for future projects and have benefitted professionals and students alike in the data science community.
Notable Achievements
Throughout his career, Mike has consistently leveraged his data science expertise to drive significant business outcomes. Some of his most notable achievements include:
- Developing Product Analytics at Google Shopping: Mike is currently focused on enhancing Google Shopping’s analytics capabilities, integrating cutting-edge technology like Generative AI and AR to improve user experiences and drive innovation within the platform.
- Data-Driven Podcast Strategy at Spotify: His predictive models at Spotify provided critical insights into the valuation of original podcast content, helping to refine content strategy and optimize marketing spend effectively.
- CausalML Co-Author at Uber: By co-authoring the CausalML package, Mike made significant contributions to marketing analytics, enabling Uber to optimize its multi-billion dollar global marketing operations through improved causal inference techniques.
- Diverse Experience Across Industries: Mike’s roles in various companies including Retina AI and Pluto TV have allowed him to apply data science principles across different domains, showcasing his versatility and adaptability in tackling various data challenges.
Conclusion
Mike Yung is a dedicated and innovative data scientist whose work has significantly impacted the businesses he has been part of. With a strong educational background complemented by extensive hands-on experience in product analytics, machine learning, and causal inference, he continues to pave the way for data-driven decision-making in the tech industry. His ongoing projects at Google Shopping promise to showcase further advancements in analytics, especially in areas involving Generative AI and AR products. Through his career, Mike has proven to be not only a skilled practitioner of data science but also a collaborative leader who is passionate about advancing the field and contributing to the growth of others in the industry.
tags':['data science','machine learning','product analytics','causal inference','Generative AI','AR products','Google Shopping','Spotify','Uber','CausalML','predictive modeling','data-driven decisions','analytics','economics','Galvanize Inc','multi-billion dollar marketing','healthcare data','content strategies','viewer engagement','analytical skills','open-source contributions','professional development','thought leadership'],
questions':['How did Mike Yung leverage machine learning to enhance product analytics at Google Shopping?','What unique strategies did Mike implement at Spotify to forecast the value of podcast content?','Can Mike Yung elaborate on his experience co-authoring the CausalML package and its impact on marketing operations at Uber?','In what ways has Mike applied his economic background to inform data-driven decisions in his career?','How does Mike Yung keep up with the evolving landscape of data science and machine learning?','What insights can Mike share from his diverse roles at companies like Uber, Spotify, and Google?']}} ortech from the ground up, emphasizing his commitment to innovation and driving data-centric decisions. Additionally, his experience spans key tech companies, allowing him to build a diverse portfolio that combines practical skills with thought leadership in data science.
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Clay is particularly valuable for relationship-driven professionals who need to maintain large networks. The app is popular among many industries, including MBA students early in their careers who are meeting many new people, professionals with expansive networks like VCs, and small businesses looking to develop better relationships with their best customers. Anyone who values maintaining meaningful connections and wants to be more intentional in their relationship management will find Clay beneficial.
Clay offers competitive pricing in the personal CRM market. Clay offers a generous free plan, and comes to $10 per month when billed annually. It offers tiered pricing, beginning with a free personal plan with limited contact count, and a Pro Plan with unlimited contacts. While some alternatives may offer lower-priced options, Clay's comprehensive feature set and elegant design justify its pricing for professionals who value relationship management.
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Yes. Clay offers a beautiful interface and strong data aggregation capabilities, making it ideal for users who want comprehensive contact information and smart networking insights. Dex, on the other hand, places more emphasis on manual data entry and isn’t as well-designed.
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