Would you leave ML Engineering for a Lead Data Scientist role that's mostly analytics?

Summary

ML Engineering versus Lead Data Scientist with a focus on analytics changes career choices for data professionals. An ML Engineer is considering an offer for a Lead Data Scientist position, but realizes that the daily tasks mainly involve dashboards, analytics, and stakeholder management. This raises questions about the value of the title versus technical depth.

What is happening

An ML Engineer at a mid-sized company receives an offer for a Lead Data Scientist role. While the position seems attractive, it primarily focuses on strategic tasks such as creating dashboards and managing stakeholders, prompting the engineer to question whether the potential benefits outweigh his current responsibilities and technical focus.

Why this matters

This situation highlights an important consideration for BI professionals: the choice between focusing on technical skills or accepting a role that includes more management tasks. As the demand for data analytics increases, it is becoming increasingly common for data scientists and ML engineers to weigh different aspects of their careers. This may indicate a broader trend where seniority and a titled position do not always equate to better career opportunities or job satisfaction in the field.

Concrete takeaway

BI professionals need to reflect on their long-term career path and determine which aspects of their work are most important to them. The choice between a technical role and one with more responsibilities can be pivotal for future opportunities and personal development.