Скачать или смотреть Running unified attribution at scale | Martin Stein @ Conversion Logix | Data Science Hangout

Join us for a conversation with Martin Stein, Chief Analytics Officer at Conversion Logix, to chat about running unified attribution at scale, the tools they leverage, languages Conversion Logix's uses, and the workflow and operations to make it all happen.

Featured Leader Bio: Martin Stein is a seasoned data scientist, entrepreneur, and Chief Analytics Officer at Conversion Logix. With over 20 years of experience in AI, machine learning, and data science, he brings a wealth of expertise to the multifamily industry, developing cutting-edge MarTech solutions that empower marketing leaders to make data-driven decisions to maximize their investments. Stein is also the co-founder of Bend.ai, an AI/machine learning company acquired by Conversion Logix. His career includes significant roles at Apple, IDG/IDC, G5, RStudio, and startups like FileThis, Defined.ai, and Union.ai. Throughout his career, he has demonstrated a proven ability to build and scale products to over $200 million in revenue.

Martin shares insights on topics like:

✨ The problem with "last-touch" attribution in marketing and how his team is building solutions to provide a more comprehensive understanding of marketing channel effectiveness.
✨ Common data science struggles, including data access, data inconsistency, production challenges, and team dynamics.
✨ The value of community in a data scientist's journey, particularly for those exploring new areas like AI.
✨ Tips for effectively communicating data science concepts and building business cases, both internally and with clients.
✨ Approaches to measuring channel success in marketing and comparing it to copy success, emphasizing the importance of considering factors beyond individual channel performance.
✨ How tools like Pins and Connect facilitate data management, workflow efficiency, and model deployment.
✨ Insights into the use of vetiver for model evaluation, highlighting its ability to assess model behavior with known and unknown data and its relevance to MLOps practices.

Don't miss this insightful episode packed with practical advice and real-world examples for data scientists at all levels! A few people in the chat said it felt like they were listening to a really great mentor speak, and we agree ❤️

Resources mentioned in the episode and chat:

🔗 Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R by Martin Huber
https://mitpress.mit.edu/978026254591...
🔗 Causal Inference: The Mixtape by Scott Cunningham
https://mixtape.scunning.com/
🔗 Kevin Ushey’s R Studio Conf talk on Renv
   • Fully containerized R dev environment with...  
🔗 Rami Krispin’s VS Code and R setup
https://github.com/RamiKrispin/vscode-r

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