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

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~19:20)

Data Science Best Practices (with Ben Skrainka ~34:50)

Original music and sounds by The Sticks.

#59 Data Science R&D at TD Ameritrade

DataFramed