AI News, Insight Data Science Fellows Program launches New York session

Insight Data Science Fellows Program launches New York session

The Insight Data Science Fellows Program is a post-doctoral training fellowship that helps quantitative PhDs transition to careers in data science and analytics.

When I started the program back in June 2012, I was trying to solve a problem that I had myself when transitioning from particle physics to the tech industry several years prior.

Friends and colleagues in quantitative fields of academia were also having difficulty bridging the gap between their existing experience with data, statistics, machine learning and coding, and applying those skills in a industry context.

The focus at Insight since our first session has been creating an educational model that leverages quantitative scientists’ years of experience with data and code, to help them effectively transition to industry.

I’m looking forward to growing the community of Insight Fellows on the East Coast, helping more academics contribute to the growing field of data science as they become active participants in the vibrant data communities of New York and Boston.

How to Attract Top Data Science Talent: Lessons from 1,400+ Insight Fellows

When Jake Klamka founded Insight Data Science in 2011, the term “data science” had a much different understanding in the private sector than it does today.

There are far more companies hiring data scientists today, and far more specialist data roles available, using a wider array of more sophisticated technology.

“I mean across the board, data is everywhere.” Insight Data Science, which helps PhDs and engineers transition to careers in data science, data engineering and AI, has grown and expanded its fellowship programs as the industry has grown.

Since its inaugural cohort of eight Fellows in 2012, Insight now has over 1400 alumni and hundreds of Fellows participating each year in specialized fellowship programs across a number of different locations, including Silicon Valley, New York, Boston, and Seattle.

Data science candidates have many opportunities to choose from, and hiring managers must clearly articulate what makes their teams appealing if they wish to secure a candidate for a role.

Insight teaches fellows new skills (like SQL or Python), as well as helps them rediscover skills they already have that they didn’t realize could be applied outside academia or their current professional role.

When you try to imagine what highly-educated data scientists like the Fellows at Insight desire in a job, consider what they’re leaving behind when they take the leap to pursue a data career.

“That may involve advanced techniques, but sometimes it may just involve really basic stuff.” Don’t fall into the trap of simply believing that getting to work with the hottest new tools will attract great talent.

Be prepared with detailed examples of the outcomes that data science has had on your business, or, if you’re just getting started with your data team, the impact you’re hoping for it to make.

He adds, “Most data scientists are impact-driven, but everyone’s idea of what kind of impact they want to make is different.” Whether a data scientist can make the kind of impact they seek will come down to the mission of your organization.

This is great for both hiring managers and potential hires because it means data scientists get to leverage not just their technical data skills, but their expertise in (and enthusiasm for) more specific subject matter areas.

“And it’s not even just them, it’s also hospitals, it’s nonprofits, the list goes on.” Companies that don’t fit into labels like “technology” or “Fortune 500” can stand out for the specialized impact that data work can have in those organizations.

The tools these alumni are working on “help doctors retrieve relevant information, such as available clinical trials, so they have it at their fingertips when trying to help patients.” Several Insight alumni have also landed at education non-profit Khan Academy, a Silicon Valley darling among free online course providers.

They’re coming in and saying to candidates, ‘Hey, we get it, many companies are doing data science, but if you come here you’re doing data science on education and helping students, or you’re doing data science on cancer research because you care about that, or you’re doing data science innovating on a retail model, because you think that’s an exciting direction where the industry’s going to go,’” says Klamka.

One of the most important parts of data science recruiting is to make sure that the managers of your data science team speak to potential hires in person.

One reason this is so important is that most job descriptions are written in broad terms, so as to attract a wide net of applicants and not accidentally screen out someone who might be great.

(“There’s a lot of ‘and, and, and,’ in job descriptions,” as Klamka puts it.) That means you need someone who really understands what your company needs in a data scientist to be part of the recruiting process, so they can explain to potential hires where they fit in.

“Our program is actually based very heavily on having the heads of data teams as well as their colleagues come in and speak directly to the fellows.” You should also consider looking at talent within your company that can be developed.

Preparing for the Transition to Data Science

Before this he was a Fellow in the 2015 Summer Insight session, where he developed an app to help social scientists write in the style of the top journal articles in their field.

After a PhD spent analyzing data and studying the Higgs Boson at the Large Hadron Collider, Kevin joined the Insight Data Science team to provide unique educational experiences and help PhDs and postdocs make transitions to careers in data science.

As part of the Insight team, we attend career panels at universities across the country to speak with scientists who are considering career transitions.

One of the most common questions we get asked is: “What skills and tools should I be learning?” Since 2012 Insight has helped 400+ of the brightest PhDs and postdocs transition into top industry positions and we’ve learned a lot about how to make the transition efficient.

Python is a general purpose programming language that has a growing number of modules for data analysis, including SciPy, Numpy, Pandas, StatsModels, and Scikit-learn, as well as many visualization tools like seaborn, matplotlib, and ggplot.

Data scientists often work closely with engineering teams and being able to understand your colleagues and teammates is crucial for doing great work.

But your goal as a data scientist should be to get better at finding the right tool, the right model, and perhaps most importantly, asking the right questions of your data.

In the tech industry, one often has to continually reevaluate priorities and work in an agile manner rather than letting projects go on for long timescales with less flexibility.

One final piece of advice we suggest is immersing yourself in the data science world by keeping abreast of the latest news in the field.

Insight Data Science Fellows Program launches New York session

The Insight Data Science Fellows Program is a post-doctoral training fellowship that helps quantitative PhDs transition to careers in data science and analytics.

When I started the program back in June 2012, I was trying to solve a problem that I had myself when transitioning from particle physics to the tech industry several years prior.

Friends and colleagues in quantitative fields of academia were also having difficulty bridging the gap between their existing experience with data, statistics, machine learning and coding, and applying those skills in a industry context.

The focus at Insight since our first session has been creating an educational model that leverages quantitative scientists’ years of experience with data and code, to help them effectively transition to industry.

I’m looking forward to growing the community of Insight Fellows on the East Coast, helping more academics contribute to the growing field of data science as they become active participants in the vibrant data communities of New York and Boston.

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