The Data Scientist Show

Emma Ding is a data scientist turned career coach. Previously she was a data scientist and software engineer at airbnb. I first discovered her through a viral Medium blog called “how I got 4 data science offers and doubled my income 2 months after being laid off". Today, her mission is to help data scientists land their dream offers by being strategic and efficient in their interview preparation at https://www.datainterviewpro.com/. Among the 80 clients she worked with, 90% of them received data scientist job offers from top tech companies, such as meta, linkedin, doordash, robinhood, etc. We talked about how she doubled her salary and got into Airbnb after she was laid off , her experience at Airbnb during the first half of the podcast, and then we’ll dive into new trends in data science interviews and her best strategy to get a data scientist job. 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

Emma's YouTube: https://www.youtube.com/c/

DataInterviewPro Free product case class: https://www.datainterviewpro.com/product-case-masterclass-registration 

Books on causal inference: Mostly harmless econometrics and Mastering Metrics: The Path from Cause to Effect. 

Emma's Linkedin: https://www.linkedin.com/in/emmading001/ 

(00:00) Intro  

(00:04:24) her strategy to get the data scientist offer after the layoff  

(00:07:00) advices for preparing interviews  

(00:14:04) her day-to-day at Airbnb  

(00:16:46) things she learnt from her mentor  

(00:18:07) from a data scientist to a SDE to a data interview pro  

(00:22:12) trends of data science interview  

(00:26:48) data scientist tracks: analytics-driven vs algorithms-driven  

(00:32:56) SQL interviews: readability and proficiency    

(00:35:06) make a study plan, execute it and keep the confidence  

(00:41:29) what she teaches in her datainterview.com  

(00:43:45) how to tackle take-home challenges  

(00:45:41) how to negotiate salaries  

(00:46:56) how to build confidence in the job search process  

(00:50:23) how to study efficiently different subjects  

(00:54:26) how to transition to data science  

(01:00:05) how to remedy mistakes during the interview  

(01:03:37) is data scientist still in demand?  

(01:08:43) advices for getting ready for the new career

Carly Taylor is a senior manager at Activision, leading a team of  machine learning engineers to tackle disruptive behaviors in the game ‘Call of Duty’. Previously, she has held various roles including machine learning engineer, data scientist, product analyst, Analytical Chemist. She has a master degree in computational chemistry from the university of colorado. She’s passionate about video games and cyber security. She shares her insights on machine learning, gaming, and career with 33k Linkedin follower. 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

Carly's Linkedin: https://www.linkedin.com/in/carly-taylor0017/

Highlights:

(00:00) Intro 

(00:01:14) from chemistry major to data scientist in gaming 

(00:05:46) how she tackles disruptive behavior using machine learning 

(00:11:38) feature engineering and model drift in fraud detection 

(00:16:49) the challenge of dealing with the large scale of data 

(00:27:10) data science in the Metaverse 

(00:36:08) signal processing and anomaly detection 

(00:40:31) dealing with the outliers 

(00:45:49) gets the buy-ins from the leadership 

(00:49:56) from an IC to a manager 

(00:53:36) mentorship, mistakes, and other things she learnt from work 

(00:58:48) Python or R? 

(01:05:30) how she sees herself grow and how she deals with struggles 

(01:07:56) the future of data science in gaming

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

David Kelly is a senior member of research staff at the Voleon Group, a quantitative hedge fund solving large-scale financial prediction problems with statistical machine learning. Previously, he was an assistant professor at New York University. He has a phD in math from the University of Warwick. Today, we’ll talk about quantitative machine learning in hedge fund and his career journey. 

(00:00) Intro 

(00:01:03) how he got into machine learning 

(00:04:01) his day-to-day as a quant 

(00:07:17) time-series model in hedge fund 

(00:10:35) data versioning in finance

(00:17:32) back testing in finance 

(00:34:02) traps and mistakes of quant ML 

(00:37:58) how to have an edge in quant ML 

(00:49:31) where does he find inspiration in algorithms 

(00:56:48) from junior data scientist to tech lead 01:09:25 interview preparation for quant jobs 

(01:15:55) common mistakes in people's early career 

(01:20:51) how he sees his career grow 

(01:22:36) the future of quantitative ML

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