The Artists of Data Science
Build A Career in Data Science | Jacqueline Nolis and Emily Robinson
Oct 5, 2020
Jacqueline Nolis, a principal data scientist at Brightloom, and Emily Robinson, a senior data scientist at Warby Parker, dive into the world of data science careers. They discuss the three types of data scientists and how to transform business problems into data challenges. The duo also tackles effective analysis strategies, transitioning models into production, and the importance of clear communication with stakeholders. Their journey co-authoring a data science book highlights collaboration, inclusivity, and the significance of foundational data engineering.
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Quick takeaways
- Data science roles can be categorized into analysts, decision scientists, and machine learning engineers, each contributing unique value to the field.
- Effective data analysis must align with business needs and be presented in a way that ensures clarity and comprehension for stakeholders.
Deep dives
Navigating Bro Culture in Data Science
Many areas within data science can be perceived as bro-y, with certain subreddits and communities promoting a macho culture that emphasizes the complexity of models and the prestige of one's educational background. This atmosphere can be intimidating to those entering the field, as they may feel inadequate if they don't engage in competitive discussions about neural networks or 'real' data science credentials. However, the podcast highlights that such attitudes do not represent the entirety of the data science field. It's essential to recognize that contributions from all backgrounds and levels of experience are valuable and to avoid letting these prevailing attitudes undermine one's confidence as a data scientist.
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