Radio DaTa

Jonas Björk works as the CTO at Acast. Before joining Acast around 5 years ago, Jonas had been working with data, analytics, and ML at Spotify, BizOne, and Ericsson.

Acast is a Swedish-founded podcast hosting and monetization platform that allows creators to distribute their podcasts to multiple podcast apps such as Spotify, Apple Podcasts, and Google Podcasts, and to monetize their content through advertising and listener support. Acast also provides analytics and data insights to help creators understand their audience and optimize their content, as well as tools for promoting and growing their podcasts. In addition, Acast offers targeted advertising solutions to brands and advertisers.

Topics that we talk about:

• Data collected and used by Acast
• Differences between measuring songs (e.g. on Spotify) and measuring podcasts (e.g. on Acast)
• Analytics use cases implemented at Acast
• Cloud-managed data tech stack at Acast e.g. AWS, Snowflake, Airflow, Python, Rust
• AI/ML in podcasting used today or tomorrow
• Trends and innovations in the podcasting industry
• The Acast's tech plans for 2023
• Interesting challenges when implementing data-specific projects in the podcasting industry

The podcast was recorded by Adam Kawa (GetInData).

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Hosted on Acast. See acast.com/privacy for more information.

Liudmyla Taranenko works as the Head of Data Science at Metadata.io.

Metadata.io builds a product for B2B marketers that automates many manual and repetitive tasks. By taking care of tasks like running paid campaigns, personalizing web experiences, and optimizing everything for revenue, Metadata.io frees up time for marketers to focus on strategy, creativity, and driving revenue.

Topics that we talk about:

• Data sources collected and used by Metadata.io
• Data-driven features and product analytics at Metadata.io
• ML algorithms at Metadata.io e.g. Neural Networks
• Tech stack used at Metadata.io e.g. AWS, Databricks, (Py)Spark, MLflow
• Interesting data science challenges and edge cases at Metadata.io
• The future of marketing driven by data and AI
• The unique aspects of working with data science at Metadata.io
• The life cycle of data science projects at Metadata.io which ends with productization, UI, storytelling, and marketing

The podcast was recorded by Adam Kawa (GetInData).

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Hosted on Acast. See acast.com/privacy for more information.

If you hear that your company should be data-driven, but you are not sure what does it mean in practice, in this episodes we share two stories of data driven companies. Both of them are the examples of data literate companies from different prospectives. In the fist story you can learn how the big tech company were allowed to detect problem and start solving the right one. The second one, how e-commerce company had prepared more effective promotion and increase revenue.This podcast episode was recorded by Adrian Dembek and Piotr Mencelewicz (GetInData | Part of Xebia).

Follow-up links:

• Data-driven survey - this survey helps you understand how data-driven your company is and identify data opportunities ahead
• Webinar: Data-Driven Fast Track: Introduction to data-drivenness

Hosted on Acast. See acast.com/privacy for more information.

Henrik Feldt is the founder, CEO and CTO of Causiq. He previously worked as a cloud or system architect at companies such as VOI Technology or Tradera.

Causiq is a marketing analytics company. It builds a product that utilizes ML to give enterprises a comprehensive understanding of the efficiency of their marketing channels. This allows them to identify ROI for each marketing channel daily (or near real-time) and offers companies immediate insights into the efficiency of their marketing activities.

Topics that we talk about:

• What Causiq is, who uses it and why
• How data & ML are used at Causiq
• The importance of real-time ML in analyzing the efficiency of their marketing channels
• Tech stack used by Causiq (the mix of GCP and open-source such as Kafka, Flink, Hudi, dbt)
• Entrepreneurial advice for data engineers/architects who would like to launch their own product or company

The podcast was recorded by Adam Kawa (GetInData).

Subscribe to Radio Data us on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Hosted on Acast. See acast.com/privacy for more information.

Kevin Goldsmith works as a CTO at Anaconda, and he had previously worked in various roles at several companies e.g. CTO at Onfido and Avvo, as well as VP of Engineering at Spotify. He is also a Board Member and Advisor at several companies.

Anaconda is the most popular open-source distribution of the Python and R programming languages for data science that aims to simplify package management and deployment. Currently, ~30M practitioners from 235 countries and regions use Anaconda in their work.

Topics that we talk about:

• What Anaconda is, who uses it and why
• Data and analytics used internally by Anaconda
• The role and responsibilities of CTO at Anaconda
• SQL vs. Python in data science
• Hiring and layoffs in the tech industry
• An agile approach to data engineering and data science projects

The podcast was recorded by Adam Kawa (GetInData).

Subscribe to Radio Data us on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Hosted on Acast. See acast.com/privacy for more information.

Michał Wróbel works as a senior data engineer at RenoFi and has over 7 years of experience in data engineering and building data platforms.

RenoFi is a U.S.-based FinTech that uses the after-renovation value instead of your home's current value, enabling you to borrow the most money at the lowest rates.

Topics that we talk about:

• What RenoFi is, who uses it and why
• Data that is used at RenoFi
• Business use cases that are developed using this data (e.g. lead scoring)
• Modern Data Platform on top of Google Cloud Platform at RenoFi
• Building more (stuff) with less (people) at RenoFi
• Good decisions made by the CTO when launching the company
• Advanced ML/AI modes or real-time analytics - build or not to build at a startup?
• Plans for 2023 at RenoFi

The podcast was recorded by Adam Kawa (GetInData).

Please share our podcasts with your friends. Subscribe to Radio Data us on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Hosted on Acast. See acast.com/privacy for more information.

Arunabh Singh lives in Stockholm in Sweden and he works as a director of data science at Willa.

Willa is a Sweden and U.S.-based FinTech that helps professional freelancers, influencers, and social media content creators get paid immediately by brands for their freelance work and paid collaborations. The company’s founders are former early members of Spotify’s growth team.

Topics that we talk about:

• What Willa is, who uses it, and why
• Data that is used at Willa and business use cases that are developed using this data
• The most important ML models implemented at Willa
• The ML(Ops) stack at Willa and a decision to build ML & Analytics capability very early
• The most important skills and competencies that data scientists should have these days
• The main trends and predictions for ML/AI for the next decades
• Plans for 2023 at Willa.

The podcast was recorded by Adam Kawa (GetInData).

Please share our podcasts with your friends. Subscribe to Radio Data us on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Hosted on Acast. See acast.com/privacy for more information.

Alessandro Romano lives in Hamburg in Germany and he works as a data scientist at FREE NOW.

FREE NOW is Europe's largest multi-mobility app where you book a taxi, electric scooter, electric bike, and other vehicles.

Topics that we talk about:

• What FREE NOW is, who uses it and why Alessandro joined it as a data scientist
• Data, techniques, signals, and KPIs used to develop the dynamic pricing ML model for a real-time mobile app
• Working with stakeholders to understand changing priorities to adapt and optimize ML models
• Collecting the feedback from users in the interactive mobile app and running experiments and A/B tests
• The technology stack used by data scientists and ML engineers at FREE NOW
• The importance of using the right (sometimes simple or state-of-the-art) techniques to solve a particular problem rather than blindly following new fancy techniques and trends.

The podcast was recorded by Adam Kawa (GetInData).

Please share our podcasts with your friends. Subscribe to Radio Data us on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Hosted on Acast. See acast.com/privacy for more information.

This time our episode title is "Future-Aware Data Engineer". It is the story of past and current inventions like Facebook by Mark Zuckerberg vs airplane by the Wright brothers. What is the Dunning-Krueger effect and what does it have in common with Wikipedia? Why did Jacek Kuroń not have to pay his phone bills?

We're going to look at the inventions through the lens of Yuval Noah Harari, Daniel Kahneman, and Slavoj Zizek. Seems like the perfect authors' trio for the ideal data-related holiday podcast.

This podcast episode was recorded by Paweł Leszczyński (GetInData).

PS. The podcast comes from one of our internal "Lunch & Learn" sessions at GetInData. You can find more topics of our "Lunch & Learn" session at GetIndata here.

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Wouter de Bie lives in New Orleans and he has been working with big data for around 12 years! Wouter comes from the Netherlands, but he has spent most of his time working with data in Sweden and USA where he worked at Delta Projects, Spotify, The New York Times, and now as a Director Of Engineering at Datadog. Topics that we talk about:

• What Datadog is, who uses it, what data it collects, and how data is used in their product
• Multi-cloud developer experience at Datadog (technology stack, cloud providers, open-source)
• Future plans for the evolution of the data platform at Datadog
• Differences between Datadog and Spotify in the context of building the data platform, goals, and challenges
• Important patterns that one can notice when working with big data for 12 years
• Gaps and areas to watch for new tools/products in the data landscape

Thanks, Wouter!

Please share our podcasts with your friends.

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