Notes on Hiring for Data Science Teams

 

1. Unicorn Myth in Hiring:

   • Companies often seek individuals with a myriad of skills for data science roles.
   • Ideal candidates possess domain-specific knowledge, excel in data analysis, and have strong presentation and storytelling skills.
   • However, finding such a "unicorn" is rare, and the focus should shift to other essential qualities.

2. Resonance with Company DNA:

   • The key is to identify applicants who resonate with the company's values and business domain.
   • While analytics skills can be taught, passion for the business is intrinsic and contributes significantly to productivity.

3. Curiosity Matters:

   • Curiosity is a top priority in hiring for a data science team.
   • Candidates should demonstrate a broad curiosity, showing interest in various aspects of their surroundings beyond just data science.

4. Sense of Humor:

   • A lighthearted approach is crucial, as being too serious may hinder collaboration and creativity.
   • A sense of humor aids in navigating challenges and fosters a positive work environment.

5. Hierarchy of Skills:

   • Prioritize qualities such as curiosity, sense of humor, and social skills over technical expertise initially.
   • Technical skills become the final consideration, as curiosity and communication skills are harder to teach.

6. Role-Specific Considerations:

   • For data scientists, technical components, data manipulation, and communication skills are vital.
   • The ability to explain complex findings in a compelling manner is crucial for effective collaboration.

7. Mathematics and Statistics Background:

   • Strong mathematics and statistics foundations are important for problem-solving and analysis.
   • Problem-solving abilities and analytical thinking are key indicators of a successful data scientist.

8. Love for Data and Analytical Thinking:

   • Candidates should display a genuine passion for working with data and possess strong analytical thinking skills.
   • Proficiency in data visualization and problem-solving is essential.

9. Understanding Team Roles:

   • Before hiring, companies must define the roles within their data science team.
   • Growth requires understanding whether engineers, architects, designers, or specialists in data manipulation are needed.

10. Technical Skills and Platforms:

    • Technical skills depend on the chosen platform and field.
    • The choice between structured data, big data, or unstructured data environments determines the necessary technical expertise.

11. Communication Skills:

    • Effective communication and presentation skills, including storytelling, are as vital as technical proficiency.
    • The ability to convey findings in a compelling manner empowers stakeholders and enhances the impact of data insights.

12. Joy of Insight:

    • Sharing insights with clients or colleagues creates a sense of joy and empowerment.
    • The ability to turn data into a story that surprises and enlightens is a hallmark of a successful data scientist.