How to build successful data science teams

Gartner said that 60% of data science projects fail to move past preliminary stages. Another report from Accenture states that three out of four C-suite executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely.

Why won’t the Data Science project scale?

In November 2019, we conducted a webinar on the best ways to choose data science projects targeting maximum business RoI. 

During the webinar, we asked the audience the reasons they think data science projects won’t scale.

33.3% of them voted for a lack of skilled people/difficulty in hiring the required role. It clearly indicates that the secret sauce to a successful data science project is a successful data science team with diverse job roles.

To address the pain point, we organized a webinar on how to make successful data science teams. The webinar was on 16th January 2020, 10:00 AM EST.

For this webinar, Ganes Kesari, the co-founder & Head of Analytics at Gramener, and Sundeep Reddy Mallu, the SVP of Products & Hiring at Gramener teamed up to share tips on how to build successful data science teams.

Ganes has years of experience in driving successful data science teams and projects at Gramener. He talked about the critical skills and roles you are missing in your data science teams. 

Sundeep leveraged his industry experience to share tips for data science hiring. The webinar significantly focused on what aspirants should know about the data science jobs and how organizations can target the right talent.

This webinar illustrated the skills and roles you must plan for and how to tailor this based on your organization’s data maturity. The session also focused on how and where to find talent.

Successful data science teams need more than just data scientists

Artificial Intelligence (AI) is booming and every organization across the spectrum, from startups to million-dollar enterprises, are realizing the benefits of AI.

According to a Gartner report, 14% of Global CIOs have already deployed AI in their business processes and 48% will deploy it by 2020. Here comes the painstaking task for CIOs to build a scalable data science team.

It is a myth that data science teams should have abundant data scientists. 

Building and executing successful, ethical, and insightful AI solutions requires a well-rounded data science team, and not just a few idealistic data scientists who can do it all.

Ganes Kesari
Co-founder & Head of Analytics, Gramener

So what essential roles and job descriptions are you missing that’s restricting you to collaborate a perfect data science team?

At Gramener, we have a perfect blend of talent. Job roles such as data scientists, data engineers, data storyteller, data translator, behavioral psychologists make a good data science team.

Watch the webinar and know how unconventional job roles combine to make successful data science teams. Ganes also talked about how your data science team can help you scale your analytics projects.

About Gramener

Gramener is a data science consulting company that rapidly builds data applications and tells insights as stories to accelerate decision making for business users. We operate across domains and are uniquely positioned to offer:

• Custom Data Science solutions in key domains (Pharma & Life Sciences, Media, Retail, Financial Services, Public Governance)
• Automated business insights narrated through data storytelling
• AI Solutions and machine learning consulting in Natural Language Processing, computer vision, satellite imagery, and more.
• Data advisory services for businesses to help them scale up their data maturity level and set strategies to get maximum ROI on their data science investments.