Data Science Practitioners’ Talk with Ali and Kai from Neuberger Berman

Continuing our Data Science Industry Practitioners’ Talk Series, we are excited to host Ali and Kai from Neuberger Berman!

About Neuberger Berman:

Neuberger Berman is a global asset management firm with over $400 billion in assets under management, serving individuals, families, endowments, foundations, and institutions. Founded in 1939, the firm employs a research-driven approach to investing, focused on creating long-term value and managing risk. Neuberger Berman offers a wide range of investment strategies across equities, fixed income, private equity, and hedge funds, and is also known for its expertise in alternative investments and ESG (environmental, social, and governance) investing.

Speakers’ short Bio:

Kai Cui is a Managing Director and Head of Data Science at Neuberger Berman. Kai leads Neuberger’s team of data scientists and data analysts and collaborates with business units across asset classes to integrate data science and data insights in investment research and strategies. Prior to joining Neuberger Berman in 2018. Kai was a Vice President at Point72 Asset Management where he focused on big data and advanced data analytics in long/short equity research. Kai began his career at Validus, a reinsurance and insurance-linked securities manager. He received a BS in Biology and Mathematics from Tsinghua University and a PhD in Statistics from Duke University.

Ali Gulamhuseinwala is a vice president and senior data science engineer at Neuberger Berman. Ali leads a team of data science engineers and works closely with the Data Science team to help with data sourcing, onboarding, processing and productionizing processes. Prior to working with the Data Science, Ali was part of technology team developing applications in C# .NET and SQL. He received his MS in Computer Science from Syracuse University.

Talk Overview:

As part of this talk we will go over how Data Science fits into the traditional asset management space and the insights it provides in investment research. We will also walk through the data engineering pipelines and describe how data from various sources is transformed and unified using various open source tools and technologies like AWS, Databricks Spark and Snowflake.

Attendance Policy:

Please note that space is limited, if you register for a ticket, we expect you to attend in-person. If you no longer able to attend, please cancel your ticket at least 24 hours prior to the event, this will give opportunity to the students on the waitlist.

Students who registered but did not attend MA Stats events for more then twice will result in not receiving career opportunities announcements in the future.

RSVP is required.  Please check the MA Statistics Newsletter for details.