Subject archive for "data-science-leaders"

The best hands-on learning experience for data science and IT leaders is a one-day free event called RevX. Reaching multiple cities, RevX will be headed your way, so you can discuss top-of-mind, vertical-specific topics related to AI and GenAI, and walk away with new, and thought-provoking strategies for implementation.

See how TripAdvisor leverages GenAI to transform billions of reviews into a virtual travel agent tool.

What if you wanted to do something really ambitious in data science–something like designing an innovative new search engine? Today, that would be a daunting task, and you’d probably need a big, highly qualified team of data scientists and programmers to bring your innovation to life. And you’d need months, if not years, to finish it.

How do you build a data science capability into a powerful force for making decisions about virtually all facets of your business? And how do you recruit, train, organize, and retrain members of your team, in a field where competition for talent is intense and growing? These are just a couple of the questions that Glenn Hofmann, chief analytics officer at New York Life Insurance Company, has confronted and mastered in his tenure there.

Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business?

One of the biggest challenges in data science today is finding the right tool to get the job done. The rapid change in best-in-class options makes this especially challenging - just look at how quickly R has fallen out of favor while new languages pop up. If data science is to advance as rapidly as possible in the enterprise, scientists need the tools to run multiple experiments quickly, discard approaches that aren’t working, and iterate on the best remaining options. Data scientists need a workspace where they can easily experiment, fail quickly, and determine the best data solution before they run a model through certification and deployment.