A Product Data Scientist in Europe, specializing in causal inference through A/B testing, faces a career decision. Their current role involves extensive use of Databricks and Python for testing app features and marketing campaigns. A new job offer as a Data Analyst promises a 50% salary increase. This new position would shift their focus to ad hoc analyses and dashboard creation using SQL in Snowflake and PowerBI. Despite the financial allure, the move raises concerns about career progression, as the tech stack at the new company isn’t cutting-edge. The scientist is also weary of the repetitive nature of A/B testing, where outcomes are often predictable. However, the clarity of value in descriptive analyses is appealing. The decision weighs the immediate financial gain against long-term career goals, like working for tech giants like Spotify or Google.
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