Data science and machine learning are different beasts. CS based Machine learning and AI seem useful in task automation. Its real value is in building IP. DATA

Data science and machine learning are different beasts. CS based Machine learning and AI seem useful in task automation. Its real value is in building IP. DATA science is more like quant consulting.It is actually a subdiscipline in management science in my opinion. The key role of a data scientist in non SV company is usually 6-fold. First understand the business problem thoroughly. Get the people,process and value stack nailed. Second and this is the most imp part. Figure out how to frame the business problem. Framing isnt as linear as it looks. But it helps save a ton of time and money if you front load time in framing problem. Substep here is to frame it as an "information" or data problem. Third articulate a strategy to gather data or information. Inhouse data available? Web scrape data, buy data, build generative models. Youd be suprised how much ML work happens in this step Fourth shape the information to a format that can be analysed. Fifth, study the hell out of this dataset- the PHD angle comes here. Very few people are systematically trained to study data with rigor and be humble and honest about their findings. Lastly connect the dots between insights and business problem at hand.too often this stops with bar graphs and some scatter plots. Thats just lazy. You need to really take ownership of the problem and educate business why the solution will help and be honest about ehat it will take to get it to work. The people, process,value stack in line 1 kicks in here in recommendation and action. A bonus point. Put some kpi to track how well your suggestions are working. Real data science is super super hard. Its like finding a real nugget of diamond in dust. Data science is a way thinking. Its a business culture to be honest.