Why data scientists are tired, six real data scientists' frustrations - The Data Scientist Show #089
Apr 17, 2024
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Data scientist Daliana interviews 6 data scientists about their frustrations. They discuss challenges in healthcare, finance, data quality, and AI. Topics include advocating for yourself, aligning with managers, and prioritizing data quality over advanced AI techniques in the pharmaceutical industry. They also talk about navigating stakeholder requests, project challenges, communication, and career growth in the banking industry.
Data scientists struggle with advocating for themselves with managers and aligning with stakeholders.
Data quality challenges in pharmaceutical industries and the perception of data science as transactional are significant frustrations for data scientists.
Deep dives
Data Scientist Meetup Highlights Challenges at Work
The episode revolves around a Data Scientist meetup where professionals openly share their work struggles. Key takeaways include discussions on advocating for oneself with managers, aligning with stakeholders, and challenges faced in pharmaceutical and finance industries. Balancing technical roles with content creation was also highlighted, emphasizing the importance of addressing basic data needs alongside advanced technologies.
Navigating Data Quality Challenges in Pharmaceutical Industry
Data scientists grapple with the lack of context in collected data for AI and ML applications within the pharmaceutical sector. Companies like Recursion Pharmaceuticals focus on building data attribution from the source, contrasting with traditional companies relying on advanced tools without resolving data quality issues effectively. Addressing data quality challenges without clear incentives remains a significant frustration for data scientists.
Data Science as a Transactional Role
The perception of data science as transactional rather than product-oriented is a common frustration among professionals. Efforts to ensure continuous improvement post-project completion and the importance of viewing models as starting points for product development were highlighted. The need for strong ML operations systems and a shift towards holistic product development in the field was emphasized.
Overcoming Imposter Syndrome in Data Science
Imposter syndrome remains a prevailing experience in the data science community, with individuals feeling inadequate or fearing job loss despite their competencies. The importance of leveraging diverse domain knowledge as a strength and embracing continuous learning was emphasized as a means to combat imposter syndrome. Encouragement was directed towards overcoming self-doubt and embracing one's unique skill set for professional growth.
Daliana interviewed 6 data scientists from her meetup in New York City. It's a unique episode where you get to hear the real frustrations of data scientists. We talked about struggles working in healthcare, finance, data quality and AI, how to advocate for yourself, and align with your managers.
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