data scientists frustrated

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data scientists frustrated
  1. Why are Data Scientists Frustrated?
  2. In a survey of 64,000 developers, Stack Overflow found 14.3% of machine learning specialists looking for a new job and 13.2% of data scientists.
  3. Why are data scientists frustrated and spending 1-2 hours a week looking for a new job?
  4. Jonny Brooks, in a KDNuggets blog, outlined for reasons that leave data scientists frustrated.
  5. Expectations Don’t Match Reality
  6. Analytical candidates get into data science because ty enjoy solving complex problems.
  7. The reality isn’t living up to the dream because companies aren’t willing to address the complicated steps needed to make the data scientist’s work pay off.
  8. Hiring a senior, hands-on data scientists is a good first step, but companies must add a data engineer and a data analyst to make that data scientist productive.
  9. Data engineers build the data pipeline and infrastructure from the ground up.
  10. The data analyst is needed for the reporting and data requests that can bury even the best data scientists.
  11. Only then does the data scientists have the data resource and the time to do the more sophisticated analysis.
  12. Politics Reign Supreme
  13. A data scientist spends 80% of their time getting, cleaning and managing the data leaving only a small amount of time for analysis.
  14. They also have to deal with the interpersonal dynamics of building a data infrastructure and algorithms that highlight flaws in current processes and products.
  15. Communication and good interpersonal skills are critical to getting the data science function out of the back room without insulting someone.
  16. They must produce value, communicate what they do with people at every level and explain everything clearly to non-technical people.
  17. Data engineers build the data pipeline and infrastructure from the ground up.  The data analyst is needed for the reporting and data requests that can bury even the best data scientists.
  18. They’re the Go-To Person about Anything Data
  19. Spark, Hadoop, Hive, Pig, SQL, MySQL, Python, R, Scala, Tensorflow, A/B Testing, Natural Language Processing and Machine Learning – data scientists are expected to know it all.
  20. The expectation is that they can answer any data-related questions.
  21. Educating associates and management on what they can and can’t do is critical for success.
  22. Matching skills to company needs may be better defined by data scientists than hiring managers.
  23. Working on an Isolated Team May Not Be Ideal
  24. Many firms build Centers of Excellence for their data science function.
  25. but is sitting in an ivory tower doing the work the most effective option for the organization?
  26. Interacting directly with the line managers can keep data scientists focused on bottom-line-oriented projects.
  27. Getting into the trenches on a daily basis is where the data scientist will have the most impact.
  28. Are you a data scientist frustrated by your current role?  Work with the Data Science and Analytics Recruiters at Smith Hanley Associates to find the right position for your skill set and your success.
  29. Paul Chatlos, [email protected]
  30. Nancy Darian, [email protected]
  31. www.smithhanley.com

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