51 min
#59 Data Science R&D at TD Ameritrade DataFramed
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- Technology
This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions.
What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out.
LINKS FROM THE SHOW
DATAFRAMED GUEST SUGGESTIONS
DataFramed Guest Suggestions (who do you want to hear on DataFramed?)
FROM THE INTERVIEW
Sean on TwitterSean's WebsiteTD Ameritrade Careers PagePyData Ann Arbor MeetupPyData Ann Arbor YouTube Channel (Videos)TDA Github Account (Time Series Pattern Matching repo to be open sourced in the coming months)Aura Shows Human Fingerprint on Global Air Quality
FROM THE SEGMENTS
Guidelines for A/B Testing (with Emily Robinson ~19:20)
Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson)
Data Science Best Practices (with Ben Skrainka ~34:50)
Debugging (By David J. Agans)Basic Debugging With GDB (By Ben Skrainka)Sneaky Bugs and How to Find Them (with git bisect) (By Wiktor Czajkowski)Good logging practice in Python (By Victor Lin)
Original music and sounds by The Sticks.
This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions.
What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out.
LINKS FROM THE SHOW
DATAFRAMED GUEST SUGGESTIONS
DataFramed Guest Suggestions (who do you want to hear on DataFramed?)
FROM THE INTERVIEW
Sean on TwitterSean's WebsiteTD Ameritrade Careers PagePyData Ann Arbor MeetupPyData Ann Arbor YouTube Channel (Videos)TDA Github Account (Time Series Pattern Matching repo to be open sourced in the coming months)Aura Shows Human Fingerprint on Global Air Quality
FROM THE SEGMENTS
Guidelines for A/B Testing (with Emily Robinson ~19:20)
Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson)
Data Science Best Practices (with Ben Skrainka ~34:50)
Debugging (By David J. Agans)Basic Debugging With GDB (By Ben Skrainka)Sneaky Bugs and How to Find Them (with git bisect) (By Wiktor Czajkowski)Good logging practice in Python (By Victor Lin)
Original music and sounds by The Sticks.
51 min
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