Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Alexis Roos

Alexis Roos
Director, Data Science and Machine Learning at Salesforce, Salesforce

@alexisroos

Alexis Roos is director of data science and machine learning at Salesforce, where he leads a team of data engineers and scientists focusing on deriving intelligence from activity data for the Einstein platform. Alexis has over 20 years of software engineering experience, with the last six years focused on large-scale data science and engineering using technologies including data engineering, entity resolution, distributed graph processing, machine learning, natural language processing, and deep learning. He has worked for SIs in Europe, Sun Microsystems/Oracle, and several startups, including Radius Intelligence, Concurrent, and Couchbase. Alexis started learning programming as a teenager and was an avid 68000 programmer. He is a frequent speaker at meetups and conferences such as Spark summit SF and East, Scala by the Bay, Hadoop Summit, O’Reilly Web 2.0, and Java One. He has also led trainings and two university-level courses on big data. Alexis is a mentor at thecamp. He holds a master’s degree in CS with a focus on cognitive sciences.

Sessions

11:50am–12:30pm Thursday, March 8, 2018
Building a contacts graph from activity data
Data engineering and architecture
 Location: 230 C
Secondary topics:  Graphs and Time-series
Alexis Roos (Salesforce), Noah Burbank (Salesforce)
Average rating: ***..
(3.00, 1 rating)
In the customer age, being able to extract relevant communications information in real time and cross-reference it with context is key. Alexis Roos and Noah Burbank explain how Salesforce uses data science and engineering to enable salespeople to monitor their emails in real time to surface insights and recommendations using a graph modeling contextual data. Read more.