Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Agile for data science teams

Jennifer Prendki (Figure Eight)
11:20am–12:00pm Wednesday, 09/12/2018
Data-driven business management, Strata Business Summit
Location: 1E 12/13 Level: Intermediate
Secondary topics:  Machine Learning in the enterprise
Average rating: ****.
(4.38, 8 ratings)

Who is this presentation for?

  • Chief data officers, data executives, and data science managers

Prerequisite knowledge

  • Familiarity with Agile methodologies and data science management (useful but not required)

What you'll learn

  • Understand why managing data teams is different from managing engineering teams
  • Learn how to adapt the planning methods and techniques that work in software engineering for data science


Since the publication of the Manifesto for Agile Software Development in 2001, Agile methodologies have been adopted by a majority of tech companies and have unquestionably revolutionized the tech industry and its culture. Agile’s huge success is hardly a surprise: Agile development came as a breath of fresh air at a time when the tech industry was crippled by the many inefficiencies caused by its own success. Back then, the Agile mindset was a panacea for tech’s growing pains.

However, the tech industry is now facing a new revolution: big data, machine learning, and artificial intelligence. The methodologies that were so beneficial to the field of software development seem inappropriate for data science teams, because data science is part engineering, part research.

Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient. Jennifer starts by discussing the Agile Manifesto in detail and reviewing the reasons for its major success in software engineering. She then outlines the different ways that organizations set up their data science initiatives and explains in which ways these teams differ or are similar to software engineering teams. Jennifer concludes by detailing how to adapt traditional Agile methodologies to create a powerful framework for data science managers and shares tips on how to allocate resources, improve best practices, and tweak the usage of planning and organization tools for the benefit of data teams.

Photo of Jennifer Prendki

Jennifer Prendki

Figure Eight

Jennifer Prendki is the vice president of machine learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance machine learning teams and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in particle physics from Sorbonne University), she likes to use her analytical mind not only when building complex models but also as part of her leadership philosophy. She is pragmatic yet detail oriented. Jennifer also takes great pleasure in addressing both technical and nontechnical audiences alike at conferences and seminars and is passionate about attracting more women to careers in STEM.

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taoufik el khiraoui | DATA SCIENTIST
09/21/2018 9:50am EDT

would you please post the slides for us to download.

09/13/2018 1:10pm EDT

Is there a place we can download the presentation from? I understood it was going to be posted on this page.

09/12/2018 3:14am EDT

Looking forward to this conference!