You wouldn’t think a data scientist would tout vulnerability and storytelling as requirements for success, but that is exactly what Jacey Heuer has learned across multiple industries and projects that have failed and succeeded. In the first of this three-part series, Heuer shares that “what you think you know today should change tomorrow because you’re always discovering something more.”

Key Takeaways

Success in data science means:

• Acknowledging that 80% of projects never make it out of production, and not because of a failure of science but a failure in communication and being vulnerable. 
• Putting yourself out there by connecting with different people. 
• Acquiring and honing new skills and behaviors that support a deeper understanding of systems thinking and the dynamic variables within those systems.
• Always iterating and reinventing. The work is never done, and it’s never easy.

Three distinctions for roles and responsibilities:

• Data Analysts work with stakeholders in-depth to understand the problems, goals, and outcomes needed.
• Data Scientists focus on prototyping and exploring and twisting and turning data – looking for the algorithm.
• Machine Learning Engineers productionalize the output.

(Read the full transcript)

Guest Bio
With a history of project experience in the financial services industry and advanced degrees in business and data science, Heuer can step into many environments and discover the knowledge needed to deliver a well-polished final product. Having gained expertise from several industries, he’s learned each one often serves as a springboard into other ones and his skill sets have ranged from simple analytic and data modeling to advanced probability theories and machine learning techniques. Heuer is also a Pluralsight author focused on the use of R in data science for business.