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What set of Skills are required for Machine Learning Jobs?
- Experimental design and analysis: Particularly for data scientists working on consumer internet applications - the way that data is logged and the way that experiments can be run gives way to a massive amount of experimentation to test various hypotheses. There's a lot of ways that experiment analysis can go wrong (ask any statistician), so data scientists can help a lot here.
- Modeling of complex economic or growth systems: Typical models like churn models or customer lifetime value models are common here, as well as more complicated models such as supply + demand modeling, economically-optimal ways to match providers and suppliers, and methods to model the growth channels of a company to better quantify which growth avenues are the most valuable. The most famous example of this is Uber's surge pricing.
- Machine Learning: Even for the data scientists that don't implement Machine Learning models themselves, there is tremendous value that data scientists can provide in helping create prototypes to test assumptions, select and create features, and identify areas of strength and opportunity in existing machine learning systems.
- Generating hypotheses: A data scientist who understands the product well can generate hypotheses about ways the system can behave if changed in a particular manner. Hypotheses are based on hunches about how certain aspects of the system can behave and one needs to know about the system to be able to have hunches about how it works.
- Defining metrics: The traditional analytics skill set includes defining key primary and secondary metrics that the company can use to keep track of success at particular objectives. A data scientist needs to know about the product in order to create product metrics that both 1. Measure what is intended 2. measure something that is worth moving.
- Debugging analyses: Results that are "incredible" are more often caused by bugs than actually incredible features of the system. Good product knowledge can help with quick sanity checks and back-of-the-envelope calculations that can help more quickly identify things that might have gone wrong.
- Communicating insights: Some data scientists call this "storytelling". The important thing here is to communicate insights in a clear, concise, and valid way, so that others in the company can effectively act on those insights.
- Data visualization and presentation: Sometimes theres nothing more effective and satisfying than a good graph at making or conveying a point.
- General communication: Working as a data scientist almost always means working as a team - including working with engineers, designers, product managers, operations, and more. Good general communication can help facilitate trust and understanding, which is incredibly important for someone who is entrusted with being stewards of the data.
- Being selfless: This includes offering help and mentorship to others, and putting the company's mission before your own personal career ambitions.
- Constant iteration: A data scientist thrives on feedback, and most parts of the data scientist's work will involve back-and-forth iteration and feedback with others to reach an impactful solution.
- Sharing knowledge with others: Since the data scientist profession is quite new, there is basically no one with the complete set of skills, especially if you collect together all of the possibly useful statistical techniques, frameworks, libraries, languages, and tools. Because knowledge will be spread out across the data scientists and the organizations, it is particularly useful for data scientists to be constantly sharing their knowledge, methods, and results with each other.