“A different perspective on what data scientists are capable of:
• Imagine dozens of scenarios and rank them by chance of occurring
• Get siloed data from various departments (finance, sales, marketing, product, IT)
• Analyze the data in connection with the scenarios (including checking data validity)
• Get external data (competitive intelligence) as needed
• Find the causes (not just correlations)
• Find the remedies
• Detect issues well before anyone else can see them, by looking in summary data
• Complete the analysis with a 48 hours turnaround
Such a data scientist who can save billions to a company, is usually not hired, for the following reasons
• Companies are looking for coders, not business solvers, when they hire a data guru, despite claiming the contrary
• A data scientist without Python on his resume is unlikely to ever get hired
• Hard work gets rewarded, smart work does not.”
Vincent Granville ( November 15, 2014 )