Ben Rogojan, known as the Seattle Data Guy, is a former Meta data engineer turned independent consultant and popular YouTuber with a strong online presence. He dives into his transition from traditional employment to consulting, sharing insights on the evolving landscape of data engineering influenced by AI and low-code tools. Ben discusses the importance of effective stakeholder engagement and personal branding in building client trust. He reflects on the diverse tech cultures of Seattle, San Francisco, and Denver while emphasizing the need for work-life balance and taking time off.

Data scientist Daliana interviews 6 data scientists about their frustrations. They discuss challenges in healthcare, finance, data quality, and AI. Topics include advocating for yourself, aligning with managers, and prioritizing data quality over advanced AI techniques in the pharmaceutical industry. They also talk about navigating stakeholder requests, project challenges, communication, and career growth in the banking industry.

Kristi Angel shares expertise on boosting experimentation velocity, designing impactful experiments, and building knowledge repos. Topics include choosing metrics, team structures, reducing noise with CUPED, future of A/B testing, and scaling experimentation programs.

Julia Silge, former astrophysicist turned data science leader, discusses challenges in survey data, Python vs. R, EDA best practices, and building MLOps tools. Topics include text analysis, balancing data science and engineering, and her journey from physics PhD to engineering manager at Posit PBC.

Wes McKinney, creator of Pandas, shares how it started, benefits of user-friendly data tools, challenges in building products, transitioning to a top architect role, improving open source, and using ChatGPT for learning, with a focus on future impact goals and career excitement.

Christopher Fricker is a senior director in analytics and BI at Renaissance Learning. He started his career in finance and later became a data science consultant working with Meta, Netflix, and pre-IPO tech companies doing analytics. We talked about the mental models that helped him grow from a finance analyst to an analytics leader. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Chris’ LinkedIn: https://www.linkedin.com/in/christopherfricker/ Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ (00:00:00) Introduction (00:01:46) How to get promoted quickly (00:08:40) Power vs authority (00:11:21) First principal thinking (00:32:34) ROI of a data team (00:40:29) How to be persuasive (00:54:52) All Data is wrong (00:56:22) How he audits the data (01:00:52) How to make someone help you at work

I interviewed Geoffery Angus, ML team lead @Predibase to talk about why adapter-based training is a game changer. We started with an overview of fine-tuning and then discussed five reasons why adapters are the future of LLMs. Later we also shared a demo and answered questions from the live audience. Try fine-tuning for free: https://pbase.ai/GetStarted Geoffrey’s LinkedIn:https://www.linkedin.com/in/geoffreyangus Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/ Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangus Try finetuning for free: https://pbase.ai/GetStarted (00:00:00) Intro (00:01:19) What is Fine-tuning? (00:08:18) Utilizing Adapters for Finetuning Enhancement (00:09:50) 5 reasons why adapters are the future of LLMs (00:26:34) Common Mistakes in Adapters Usage (00:28:34) Training Your Own Adapter (00:32:23) Behind the Scenes of the Adapter Training Process (00:37:51) Config File Guidance for Fine-Tuning (00:39:41) Debugging Strategies for Suboptimal Fine-Tuning Results (00:42:23) User Queries: Creating a LoRa Adapter and Future Support (00:51:06) Key Takeaways and Recap

Jay Feng created a viral project using Seattle crime data and later got into data science. He later founded "Interview Query" helping data scientists get jobs. We'll talk about how he landed his data science job through his blog, and his journey from data scientist to founder. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ Jay Feng's LinkedIn: ⁠https://www.linkedin.com/in/jay-feng-ab66b049/⁠ Jay Feng's YouTube: ⁠https://www.youtube.com/c/DataScienceJay⁠ (00:00:00) Introduction (00:01:11) From engineer to data scientist (00:03:10) Got a job through a project (00:05:35) Daliana's portfolio project with Zillow (00:09:13) From data scientist to entreprenuer (00:13:19) "Tinder" for job (00:15:01) How he chose companies to work for (00:15:56) Why he became an entreprenuer (00:17:37) How many hours does he work (00:18:54) Challenges when building "interview query" (00:20:18) Speed vs scale (00:22:11) Growth hacks he used (00:24:22) YouTube vs newsletter (00:27:21) Lessons he learned as a CEO (00:29:16) How to grow from tech employee to founder (00:31:59) How he defines success (00:34:38) If you have a business idea for Jay

Discussion on GenAI projects, stable diffusion models for social media apps, AI in biotech, and scaling ML teams. Insights on self-supervised learning, research vs production, AGI, and data quality in GenAI. Erik's journey from biologist to ML consultant, mistakes made, and new trends in GenAI. Philosophy in LLMs, OpenAI vs Open Source, and how he hires people.

Jay Feng is the CEO of interview query, a service that help data scientists get jobs. Previously he worked as a data scientist at Nextdoor, Monster. We talked about data science job market, the rise of AI engineering, and the softskills people overlook during interviews. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Jay Feng's LinkedIn: https://www.linkedin.com/in/jay-feng-ab66b049/ Jay Feng's YouTube: https://www.youtube.com/c/DataScienceJay 00:00:00 Introduction 00:01:11 Data science job market in 2024 00:09:13 Build projects with AI 00:16:19 Softskills in interviews 00:23:18 Daliana's story on "socializing ideas" 00:28:38 Common mistakes in interviews 00:35:30 Product DS vs ML interviews 00:36:27 Product analytics interview questions 00:39:18 Career transition in DS 00:43:04 Jay's career journey 00:45:38 Is there a principal data analyst? 00:51:52 AI engineer 00:54:28 New roles vs obsolete roles in DS 01:04:46 Is data science dead?