This question was posted on our LinkedIn group. Here's my answer
How do you define successful? Is it based on income (as an employee), revenue (as an entrepreneur or investor), or based on how much ROI you provide to your clients / employers?
In my opinion, success = consistently providing incremental lift, regardless of model used, in at least one specific field (e.g. fraud detection) across multiple applications and varied data sets (mostly big data) over a period of 2+ years, using mostly simple, scalable, replicable and stable models and implementations. Part of the success is also in seeking the right data (including external data sources), the right compound or base metrics, understanding and cleaning the data, and quickly detecting the core of the problems in a way similar to a lean six sigma approach. Part of the success is also being able to guess what executives want to address or accomplish (requires good communication and listening skills).
Now the difficult part is how to measure incremental lift: for instance, in fraud detection, it could be reduction of false positives to such an extent that it is worth to pay you your salary, and that natural causes for decrease in false positives have been ruled out. The value of a (successful) data scientist is easier to measure in some sort of A/B testing or DOE (design of experiment) framework. Ironically, the people most qualified at correctly assessing the value of a data scientist (or any employer) are... data scientists.
What is your answer?
Related topic: What is a Data Scientist?
Hi Vincent, I authored a Gartner research note on the topic of what discriminates a data scientist from say a BI analyst or statistician, Emerging Role of the Data Scientist and the Art of Data Science, and a blog summary of the piece, Defining and Differentiating the Role of the Data Scientist. These define not only the hard skills, but also the soft skills required for success. --Doug Laney, VP Research, Gartner, @doug_laney