The rise of big data analytics in recent years has created a compelling opportunity for scientists and engineers with machine learning skills to become data scientists and data engineers at fast growing tech companies. Unfortunately, for many academics and engineers, there exists a skills gap between their quantitative, machine learning and computer science fundamentals and the practical experience sought by leading data teams.
This talk will focus on strategies that engineers and academics, who are not currently working as data engineers or data scientists, can use to break into the industry. Best practices of leading data teams will be discussed, along with specific approaches you can take to leverage your engineering, quantitative or ML experience to become a successful member of one of these teams.
Jake Klamka is the founder of the Insight Data Science Fellows Program, a postdoctoral training fellowship which helps quantitative PhDs move into careers in data science. There are now more than 70 Insight Fellows working as data scientists and data engineers at companies including Facebook, LinkedIn, Square, Microsoft, Palantir, Airbnb, Jawbone, Intuit, Netflix and various startups. This summer Insight is launching the Insight Data Engineering Fellows Program, tailored specifically to professional engineers (PhD NOT required) who want to become big data infrastructure engineers at a leading tech companies. You can find more at: http://insightdatascience.com (for scientists) and http://insightdataengineering.com (for engineers).