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hopefully apple has fired you by now

ROI is horrible, but I doubt anybody is doing a PhD for ROI.

I love Tina Huang's enunciation in English, it's so addictive to listen to that's how impactful your influence is if your enunciation is so intelligible. Hope she can produce a podcast of hers soon.

And how to hold the mic, he is really doing it well

This has been insightful. Thanks for sharing people's stories! 🙌🏾

Great interview with Wes McKinney. Thanks!

So she quit DS job for RUclips completely?

Do not invest in data science career if you are not chinese. America is in direct competiton with china in data science and that is the reason most data science teams are exclusively chinese. Plus automation is another threat to keeping skills and compensation capped.

This was very helpful😊

Always fascinating how similar different paths can be. On one level consulting and academia couldn't be more different. On another level, both provide essentially the same foundations that Jason describes here.

Very interesting!! Looking forward to more contents from you!

Typically, you have to be uber chill to get into Netflix!

Love the positivity Barry!!! As more and more companies improve their data quality the impact of data scientists will keep growing

Great interview and guest. Just some advice, the editing made talking points glitchy. Try to not have so many cuts. They cause jumps and make it hard to listen to.

Very informative and helpful content. Thank you very much!

Thank you!!!

thank you

I’m a recruiter in this space and behavioral interviewing and behavioral analysis in the interview is one of my differentiators as in many cases the requisite technical skills is table stakes. Attitude and ability to work well with others is key. Most people that don’t last 18 months in a job are not issues with technical skills but it’s behavioral.

This amazing podcast ✨️📚

By YouSum Live 00:00:00 Building tools for data science production. 00:01:17 Evolution from engineering to data science. 00:02:58 Challenges in transitioning models to production. 00:06:00 Importance of experimentation for model trust. 00:10:24 Utilizing reinforcement learning for pricing. 00:14:31 Bridging gap with self-serve data science tools. 00:15:08 Enabling real-time data processing for models. 00:18:01 Connecting data science to customer impact. 00:19:15 Importance of model performance in production. 00:19:47 Challenges of transitioning models to production. 00:20:06 Balancing offline and online model performance. 00:21:25 Real-time validation crucial for model success. 00:22:28 Collaboration between data scientists and engineers. 00:23:03 Enabling data scientists to debug and monitor systems. 00:23:39 Evolution of data science team roles in startups. 00:24:45 Hiring diverse data science personas based on growth stage. 00:28:01 Tailoring data science roles to company size and attitude. 00:30:14 Collaboration tips for data scientists and ML engineers. 00:35:26 Specialization trends in data science and ML engineering. 00:36:09 Impactful projects: orchestrator, feature store, model serving stack. 00:38:12 Engineering systems aiding data scientists. 00:38:30 Turing: an ensembler and router for predictions. 00:39:02 Impactful use cases: fraud prevention, food delivery. 00:40:00 Career path from Gojek to leading Feast at Tecton. 00:41:18 Feast: open-source equivalent to Tecton's enterprise product. 00:42:02 Feature stores: bridge between data and ML models. 00:42:16 Data freshness critical for serving ML models. 00:43:00 Monitoring data consistency in online and offline environments. 00:45:08 Feature store aiding in real-time data ingestion. 00:47:58 UI, logging, and streaming enhancements in Feast. 00:50:18 Best practices: avoid real-time computations, start simple. 00:53:02 Decoupling feature engineering for flexibility and reuse. 00:54:24 Post-processing model outputs: loop between outcomes and predictions. 00:55:59 Validation through POCs and integration testing. 00:56:31 Role as a principal engineer: focus on PRDs and architecture. 00:57:28 ML Ops product development challenges. 00:59:49 Balancing technical debt for speed. 00:59:50 Importance of ownership in tech debt. 01:00:55 Code reviews for tech debt prevention. 01:01:02 Assigning area owners for codebase sections. 01:01:45 Tools like Great Expectations for data quality. 01:02:03 Impact of dbt in analytics workflows. 01:03:29 Considerations before adopting new tools. 01:05:04 Shifting focus from engineering to strategy. 01:07:40 Challenges in scaling a team's execution. 01:11:01 Communicating project uncertainties effectively. 01:12:24 Importance of aligning with stakeholders upfront. 01:14:34 Seeking healthy team dynamics in job search. 01:16:01 Evaluating company fit for data scientists. 01:16:39 Assessing project life cycles and team seniority. 01:17:44 Shift towards modern data stack and ML tools. 01:18:52 Consolidation of logic in cloud data warehouses. 01:20:00 Importance of engineering concepts for data scientists. 01:20:25 Understanding domain use cases for competitive edge. 01:21:11 Decline of Hadoop stack and rise of cloud data warehouses. 01:23:38 Embracing purpose-built tools over one-size-fits-all. 01:30:10 Future of ML engineering: open source collaboration. 01:34:01 Hiring criteria: software development skills and communication. 01:34:50 Importance of communication and persuasion skills. 01:35:07 Hiring based on potential over experience. 01:35:24 Excitement about moving to New York for new events. 01:35:34 Promoting MLAPS updates on Twitter and LinkedIn. 01:35:57 Online presence on Twitter and LinkedIn for updates. 01:36:09 Learning and gaining insights from the guest. 01:36:12 Appreciation for the guest's contribution. By YouSum Live

Awesome 🎉thanks for content amazing

Thanks for sharing about salary negotiation, wish I have the same courage 10 years ago. I should be earning at least double of what I am earning based on my skills and experience

I wonder why does it take too long though? Is it really necessary to take 4-6 to complete a PhD?

Very inspirational and helpful! Thank you both.

💔 "promo sm"

is a masters in economics/statistics a good degree to break into data science

I was reading his book couple days ago, I just got introduced to him through random github book repo. Thank you for this video :) and Hello Mr. Wes!

😊

"in order to drive a car, you need to learn how to drive a car" kind of comment

Thanks so much for the insights! I’m a data analyst with 7 yoe who’s a bit lost on the next career move😅. Wanted to go into DS but kept getting rejections bcs of no commercial experience (i did masters in DS which doesnt seem to help). Will def try to go to product manager or analytics manager role rather than keep pushing into DS/ML realm🥲

Interesting insight on why we don't see a Principal Data Analyst whereas we see Pricipal SWE/Data/MLE/DS as these roles can be grown in terms of complexity which may not be the case for Data Analyst.

As someone who's trying to move from SWE to DS/ML roles, this was definitely helpful.Thanks. One request, can you bring on someone who have made the above transition and how's the journey is.

60-day extended free trial for listeners: hex.tech/dsshow let me know what you think!

Why less views on such informative video.

Great interview, really resonate with the reality in Brazil's data scene.

How to deal with identity crisis... i felt that

Thanks for having me Daliana!

Well articulated by Barry on the ROI of a data team - internal NPS i.e. if stakeholders recommend your data team/product built by your team then thats a win for a data team hence the real ROI.

Worked with Tommy at Airbnb, he was awesome, and yes I remember this energy! too many JSON files lol

He lost all his hair after the photo!

Awesome guest! Very good work!

Typical SF "male" right there. What a disaster the Bay Area has become. There are no more real men left in that area.

Worth watching. Thank you for Valuable advices🤝

a great deep dive from Sid, this answered so many of my questions I had for a while. Thank you for the episode, Daliana.

Great production!

Great 🎉

What an inspiring guy, I enjoy your show a lot Daliana!

This was an awesome informative discussion! Enjoyed the role playing part in particular!😀

I would love to watch short videos (5-7 mins) rather than longer ones. Thank you!

Amazing! I've learned a lot about building my career. Thanks for the valuable insights!