A deep dive into data scientists' day-to-day work, tools and models they use, how they tackle problems, and their career journeys. This podcast helps you grow a...
From Meta to independent data consultant, Seattle Data Guy moved to Denver, Ben Rogojan, the data scientist show #090
Ben Rogojan, aka, Seattle data guy is an ex-Meta data engineer turned data engineering consultant and YouTuber. He has 100k followers each on YouTube and LinkedIn. We talked about how he become an independent consultant and the current state of data engineering.- Ben’s LinkedIn: https://www.linkedin.com/in/benjaminrogojan/- Daliana’s newsletter on career growth and personal branding: https://dalianaliu.kit.com/profileHi! I'm Daliana Liu Join 20,000+ subscribers to read the career lessons from my 7-year experience at Amazon and my solo-founder journey.
--------
1:02:13
Why data scientists are tired, six real data scientists' frustrations - The Data Scientist Show #089
Daliana interviewed 6 data scientists from her meetup in New York City. It's a unique episode where you get to hear the real frustrations of data scientists. We talked about struggles working in healthcare, finance, data quality and AI, how to advocate for yourself, and align with your managers.
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/
--------
42:22
Why 80% of A/B tests fail, how to 10X your experimentation velocity - Kristi Angel - The Data Scientist Show #088
Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below)
Kristi Angel’s LinkedIn: https://www.linkedin.com/in/kristiangel/
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/
(00:00:00) Intro
(00:01:26) Why do most experimentations fail?
(00:07:05) Mistakes in choosing metrics
(00:10:05) Is revenue a good metric?
(00:13:18) Split metrics in three ways
(00:15:10) Daliana's story with too many category breakdowns
(00:16:59) What makes the best data science team?
(00:19:24) Data scientist work in silo vs in a data science team
(00:21:15) Building a knowledge center
(00:23:40) Example of knowledge center; nuance of experimentations
(00:26:09) How many metrics and variants?
(00:30:56) How to reduce noise - CUPED
(00:33:01) Future of A/B testing
(00:38:33) Q&A: Low statistical power
--------
43:46
From physics PhD to data science leader, unexpected challenges in survey data, Python vs R, EDA best practices, building MLOps toolkit - Julia Silge - The Data Scientist Show #087
Julia Silge is an engineering manager at Posit PBC, formerly know as R-studio, where she leads a team of developers building open source software MLOps. Before Posit, she finished a PhD in astrophysics, worked for several years in the nonprofit space, and was a data scientist at Stack Overflow where some of her most public work involved the annual developer survey. We talked about MLOps tools, challenges in survey data, text analysis, and balancing her interests in data science and engineering.
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/
(00:00:00) Introduction
(00:00:56) Getting into data science
(00:04:50) Transition from data centers to engineering manager
(00:14:04) Common challenges in tool development
(00:17:38) Challenges with survey data
(00:26:47) Engineering skills for data scientists
(00:28:59) Balancing roles
(00:34:49) Developing skills in Exploratory Data Analysis (EDA)
(00:39:19) Python vs. R for data analysis
(00:44:40) Exciting aspects in career and personal life
--------
46:18
Why he created Pandas, the future of data systems, why he left his CTO role to become a chief architect - Wes McKinney - The Data Scientist Show #086
Wes McKinney is the co-creator of pandas library and he is the cofounder of Voltron data. Currently he is a principal Architect at Posit and an investor in data systems.
Daliana's Twitter: https://twitter.com/DalianaLiu
Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/
Wes' LinkedIn: https://www.linkedin.com/in/wesmckinn/
(00:00:00) Introduction
(00:00:44) How Pandas Started
(00:06:40) Voltron Data
(00:10:03) Benefits of Easy-to-Use Data Tools
(00:13:20) The Rise of New Data Tools
(00:18:07) Choosing Tools: Vertical or Flexible?
(00:23:01) Big Models and Data Tools
(00:29:29) Challenges in Building a Product
(00:31:28) Becoming a Top Architect
(00:34:55) Missed Aspects of Previous Roles
(00:39:04) A Busy Week: Advising, Designing, Investing
(00:43:42) Improving Open Source
(00:45:24) How to Decide What to Work On
(00:46:28) What he’s learning now
(00:47:56) Excitement in Career and Life
(00:48:29) Using ChatGPT for Learning
(00:50:27) Future Impact Goals
A deep dive into data scientists' day-to-day work, tools and models they use, how they tackle problems, and their career journeys. This podcast helps you grow a successful career in data science. Listening to an episode is like having lunch with an experienced mentor. Guests are data science practitioners from various industries, AI researchers, economists, and CTOs of AI companies. Host: Daliana Liu, an ex-Amazon senior data scientist with 180k followers on Linkedin.
Join 20k subscribers at www.dalianaliu.com to learn more about data science, career, and this show. Twitter @DalianaLiu.
Australia