| | JUNE 20219CIOReviewunicorn because of the skill set required for hitting the ground running and save the day and the budget.A data scientist needs to:- Understand the process in which she will act and that most likely she will change- Understand the business that will be the primary client demanding results- Use a technology that will fit into the company's IT department without too much disruption- Be able to provide self-standing, low maintenance, high performing software solution- Oh yes, crunch the data and create performing ML models in few times in order to be a market first moverIn short, a data scientist should have a background in Math/Physics to build the model, an IT architect to turn it into a performant solution, a lean expert to review correctly the overlapping process, and a business consultant to turn a mathematical result into a business suggestion. I'm sorry if this isn't the unicorn Wikipedia definition, but anyway looks like it's difficult to find it; we're back again to the beginning since you need to pay a lot for rare goods!The above was my fresh start in the current company! To build a data science department with very few operative knowledge about it but with big expectations in terms of results: yes, I faced all the issues I described.Since I'm still here, I probably found one of the ways to succeed. I hope you can benefit from them:You will most likely not find a person expert in your own business, so don't make this blocking. You just need to have an organization flexible enough to welcome a new member, teach the basics to survive, and remove fences from other teams' backyard.Your processes will need to change so accept it and do not be afraid of change; data must be collected correctly, and probably you are not doing this right unless you already have a data expert inside; results must fit into processes,and probably yours are not ready for a machine to provide them.Unless you're a startup, your technology is probably outdated, so be ready to adopt a new one. In data science, you have two choices: Python or R. Some years ago, just one had the correct set of tools to be production-ready, and since "it works on my machine" it's not an option; I had really just one choice and was Python. Nowadays, it's possible that also Java, .NET, or other languages will have a surrounding framework rich enough to not start models from scratch but pay real attention to the community: you can't afford writing all from scratch.Writing models (and understanding them) is surely necessary, but a background in coding with production quality is important; a suitable tradeoff is to accept lees knowledge in core data science for a good understanding of software engineering.Finally, yet importantly, the ability to explain and talk to a different audience is key in succeeding: at least one person in your geeks' team need to have communication skills.Seniority is needed but not mandatory for all team members; you can grow your team in the same timeframe that a senior person will be productive in the company. Later on, you will have more effective bandwidth. Data Scientist sometimes is considered a unicorn because of the skill set required for hitting the ground running and save the day and the budgetIacopo Ghisio
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