![]() ![]() ![]() Value add certainly doesn't have to be in dollars, but also quantifying value isn't an exact science either. it is not so easy to take a model's performance, or a performance delta, and translate that into value add in terms of dollars. ![]() I would also argue that you should be on the lookout for opportunities that others have not thought of.regardless of role (DS, engineer, etc.) So you are correct that you may not be the catalyst for a project you will have to carry the ball most of the way. I had an engineer ask me to figure out when an asset class was going to fail - but they don't replace them when they fail - they replace the asset when a certain test value crosses a threshold - predicting 'failure' would have been pointless)Ģ) identify if it's a solvable problem with the data at handĤ) educate the stakeholder to what you actually did Identifying the business opportunity begins with a stakeholder (eg a customer service rep asking a question to be answered with data) but as a data scientist it's on you toġ) make sure they're asking the right question (eg. To add a bit of clarity to my statement and to address your comment. ![]()
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