Daliana's Game

Pauline Chow is a data scientist and former legal attorney and active transportation advocate. She worked in banking, fashion and education start-ups, and Amazon. Currently, she is the data engineering lead for Thrackle, a blockchain research and modeling company. She has a master degree in computer science, Machine learning, from Georgia Institute of Technology, she also has a law degree JD from the university of wisconsin. She is also a certified yoga teacher and published writer.  We talked about her projects in three different teams in Amazon: devices, HR, and real estate; how her law degree helped her become a better data scientist; how she 're-invented' herself. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Her author website www.paulinechowstories.com or connect with her on twitter @itspaulinechow. Pauline's Linkedin: https://www.linkedin.com/in/paulinec/ A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas -- examples of the purpose of questioning. The Four Tendencies by Gretchen Rubin (quiz, book). An interesting framework for considering how different people respond to internal and external expectations and pressures. Why only rewarding high-performers can be detrimental to an organization? Wharton People Analytics Conference. Case Studies: Network Analysis. (2015, December 13). https://www.youtube.com/watch?v=0fM6JYC2zfQ

Avery Smith is a data science consultant and career coach at Data Career Jumpstar, and TA at MIT professional education. Previously, he was working on optimization and predictive analytics at ExxonMobil. We talked about his journey from from chemical engineer to data analytics, optimization problems in energy sector, why he left ExxonMobil, and his best advice for people to get into data science. Follow Daliana on Twitter (https://twitter.com/DalianaLiu) for more on data science and this podcast. If you like the show, subscribe and give me a 5-star review :)  Topics: His first data science projects His experience with ExxonMobil Why he left ExxonMobil Data science consulting Challenges when working with clients Why he built his own career coaching program How Linkedin helped his career TA at MIT, MIT's data engineering curriculum how to build a data science portfolio Avery's Linkedin: https://www.linkedin.com/in/averyjsmith/

Vin Vashishta is a chief data officer and AI strategist at V Squared, a company he founded in 2012 that  provides AI strategy, transformation, and data organizational build-out services. He teaches data professionals about strategy, communications, business acumen, and applied machine learning research methods. Vin has 130k+ followers on Linkedin talking about AI, analytics, and strategy. His website: https://www.datascience.vin/ If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Highlights: (0:00) Intro  (00:03:37) "ML strategy" with 'pricing' as an example  (00:09:45) what is a good metric for ML  (00:13:16) how to translate a business problem into a data problem  (00:23:42) leverage users in the "Human Machine Teaming"  (00:48:22) how he earned the trust  (01:17:31) data science evolution from 2012 to 2022  (01:31:06) how he learns new domain knowledge (01:36:25) the mistakes he made  (01:42:15) what he learnt from his mentor

Greg Tanaka is a computer scientist turned CEO of an AI company. He started coding when he was 6, studied computer science at UC Berkeley, and has built many machine learning applications, he is the the founder and CEO of Percolata developing ”Forecast as a Service”. He is also the council member of Palo Alto in California, and just finished his campaign for congress. Today we’ll talk about his career journey, forecasting, machine learning in blockchain and political campaigns. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Greg's Linkedin: https://www.linkedin.com/in/gltanaka/, Twitter: https://twitter.com/GregTanaka Greg's DAO: https://www.gregtanaka.org/dao Highlights: (00:02:10) use computer vision, audio, and Wi-Fi fingerprints to forecast the retail store traffic  (00:21:55) why time series forecast is hard  (00:26:39) how he made the forecasting more stable  (00:28:46) how he troubleshot the spikes and drops in data  (00:36:04) human trading vs algorithmic trading  (00:47:36) his vision of machine learning in blockchain  (00:54:57) why he got into politics  (01:05:57) advises for people who are interested in Web3  (01:11:04) AutoML and the future of machine learning (01:15:36) things he wished he could learn earlier

Jesper Dramsch is a scientist for machine learning at the European Centre for Medium-Range Weather forecasts. They have a phd in applied Machine Learning to Geoscience from Technical University of Denmark. They are a Kaggle Kernals Expert and TPU star, ranking at top 81/100k worldwide. We talked about weather forecasting, things they learned from Kaggle, how to deal with missing data and ourliers, deep learning, Keras vs Pytorch, XGBoost, their struggles as a phd student, working in the EU vs US. Follow @DalianaLiu for more updates on data science and this show. (00:01:27) how he got into in ML  (00:09:10) how he handled missing data  (00:28:34) Transformers are eating the world  (00:49:36) Hoover Loss is a fantastic metric to deal with extreme values  (00:54:48) his experience with Kaggle competition  (01:02:59) Kaggle tricks that helped his models perform better  (01:08:18) PyTorch vs Keras  (01:30:30) working in different countries and cultures  Resources shared by Jesper: The newsletter with missing data: https://buttondown.email/jesper/archive/towels-have-quite-a-dry-sense-of-humor/ The paper by Gael about missing data: https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac013/6568998 The Huber Loss: https://en.wikipedia.org/wiki/Huber_loss Skill Scores: https://en.wikipedia.org/wiki/Forecast_skill Brier Skill in Weather: https://www.dwd.de/EN/ourservices/seasonals_forecasts/forecast_reliability.html CRPS Continuous Ranked Probability Score https://datascience.stackexchange.com/questions/63919/what-is-continuous-ranked-probability-score-crps ConvNext, Convnets for the 2020s: https://arxiv.org/abs/2201.03545 Transformers for ensemble forecasts: https://arxiv.org/abs/2106.13924 Books I recommend: https://www.amazon.com/shop/jesperdramsch/list/2DYS5KVR5TX0E Blog posts I wrote about these books: https://dramsch.net/tags/books/ Short I made about Test-Time Augmentation https://www.youtube.com/shorts/w4sAh9lKyls Their links: https://dramsch.net/links Their open PhD thesis: https://dramsch.net/phd Newsletter: https://dramsch.net/newsletter Twitter: https://dramsch.net/twitter Youtube: https://dramsch.net/youtube Linkedin: https://dramsch.net/linkedin Kaggle: https://dramsch.net/

Susan Shu Chang is a principal data scientist at clearco, helping ecommerce founders' by building machine learning-powered investing. In her previous role, she developed the company’s very first ML powered website recommender system, deployed to millions of customers, and created a custom OpenAI Gym environment for a reinforcement learning project in production. She is also the founder and developer of Quill Game Studios, selling ~10k copies of the debut game in 6 months. She has given talks at PyCon Canada,Toronto Machine Learning Summit (TMLS), and more. She writes about her career journey and learning on https://www.susanshu.com/ If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Highlights  (00:00) Intro  (00:01:29) from economics to data science  (00:07:23) reinforcement learning (RL)  (00:20:00) recent reinforcement learning use cases  (00:27:28) reinforcement learning for social media's recommender system  (01:04:42) common mistakes when productionizing models  (01:08:30) principal data scientist's day-to-day (01:14:05) what productivity really means  (01:21:04) productivity tips  (01:41:48) books and blogs on productivity

Laura Gabrysiak is a senior manager of data products and solutions at Visa. Previously, she's a data scientist, building machine learning models and decision tools to enable Visa clients. She has a college degree in computational and linguistics and has masters in design thinking. She's building the local data science community in Miami, and a co-founder of our Ladies. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Laura's Linkedin:https://www.linkedin.com/in/lauragabrysiak/ (00:02:43) her journey into data science  (00:20:28) anecdotes vs big data  (00:27:05) the power of small data  (00:30:41) design thinking key elements  (00:47:25) mindset shift from a user researcher to a data scientist  (01:00:51) how to improve customer engagement  (01:02:10) how to make data visualization effective  (01:27:21) mindset shift from an individual contributor to a manager  (01:40:43) advices for people who are on PIP 

Nick Handel was a senior data scientist leading the launch of the data side of this Airbnb Trips and later built a team that designed aribnb’s end-to-end machine learning platform, bighead. Currently, he is the cofounder and CEO of Transform, he first centralized 'metrics store' that empowers data analysts to deliver insights. He was recognized as 30 under 30 by Forbes in 2018. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Nick's Linkedin:https://www.linkedin.com/in/nicholashandel/ Highlights:  (00:00) intro and career journey  (00:10:58) common mistakes in A/B testing (00:25:48) how to do A/B testing deep dives (00:27:32) surprising A/B testing results (00:29:18) facts vs opinions (00:33:55) A/B testing best practices (00:55:01) how he built a new data schema for Airbnb Trips  (01:00:43) how to collect data when building data science tools (01:38:53) trend of data science tools 

Pavel is a decision scientist and co-founder at Pytho, using decision science to measure and improve human judgment & prediction. He has a phd in psychology and decision science from the University of Pennsylvania, focusing on crowd predictions. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Pavel's twitter: https://twitter.com/PavelDAtanasov Superforecasting book, based on the Good Judgment Project: https://www.amazon.com/Superforecasting-Science-Prediction-Philip-Tetlock/dp/0804136718 Blogs about forecasting:   Vox's Future Perfect series: https://www.vox.com/future-perfect Astral Codex Ten: https://astralcodexten.substack.com/ Highlights:  (00:01:10) how he got into decision science  (00:14:38) what makes someone a super forecaster  (00:16:20) three elements of becoming a super forecaster  (00:24:37) how to effectively update our opinions  00:30:05 how he designed experiments to find out what was a better system  (00:48:27) why humans sometimes are better than algorithm  (01:14:50) how to collect data and information better  (01:33:25) why you should quit  (01:42:30) the future of decision science  

James Verbus is Staff Machine Learning Engineer at LinkedIn. He has a PhD in Physics from Brown university. He is the tech lead of the Anti-Scraping and Automation AI Team, working on protecting LinkedIn's Members from bots and abusive scripted behavior, pioneering the use of deep learning to detect abusive automated sequences of user activity (blog post). If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science. Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu (00:01:14) from physic to data science  (00:16:37) background of online abuse detection  (00:24:40) Isolation Forest Algorithm (00:42:59) his day-to-day as a staff ML Engineer  (00:52:57) how to persuade stakeholders  (00:58:17) how to build influence at work  (01:00:22) how he grew to staff engineer  (01:13:48) what he learned from his mentor