Data Science Worker
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A Data Science Worker is a data scientist in the role of being a knowledge worker in a data science job.
- Context:
- They can (typically) be a member of a Data Science Workforce (within a data science labor market).
- They can (often) be in a Data Scientist Job Role (with a data scientist JD).
- ...
- They can range from being a Junior Data Scientist to being a Senior Data Scientist (depending on their data science skill level and data science experience)
- They can range from being a Tactical Data Science Worker to being a Strategic Data Science Worker.
- They can range from being a Data Science Employee to being a Data Science Freelancer.
- ...
- They can have Data Science Education.
- They can go through Data Scientist Onboarding Task.
- They can be managed by a Data Science Manager.
- They can be similar to a Machine Learning Engineer.
- ...
- Example(s):
- Counter-Example(s):
- See: Employment Contract.
References
2017a
- http://linkedin.com/pulse/zuckerberg-test-applied-hiring-data-scientists-george-roumeliotis
- QUOTE:
- They know the techniques of Data Science. In particular, they know the theory behind the techniques. So when I need to bounce an idea off them, we can have a deep and meaningful discussion. They have some serious intellectual horsepower.
- They know the tools of Data Science. They are self-sufficient in accessing, manipulating, and analyzing data. So they are not going to be leaning on me to pull data for them, etc. And they are not religious about the use of SAS any specific tool.
- They can think at different altitudes. That is, they are equally comfortable dealing with both the big picture and the fine details.
- They are superb communicators. They explain their ideas clearly and succinctly. They ask others for input. They listen carefully. They build on the ideas of others. Their presentations are not death by powerpoint.
- They can give and take criticism well. They don’t get defensive when you challenge their ideas. In fact, they welcome input, and see how they can weave it into their ideas. Conversely, they are not afraid to challenge your ideas, but they do so gently.
- They are confident but not arrogant. They ask for help when they need it. When they don’t know something they just say so. They don’t fall in love with their own ideas. They are not territorial. They value and celebrate the contributions of others.
- They get things done. They collaboratively create thoughtful plans, communicate them to others, hustle to get resources, and then execute. They are organized. They strongly favor simple and elegant solutions over complex ones. They don’t need to display their technical prowess just for the hell of it.
- They make the people around them better. They love to learn and teach. They are patient when explaining things to others.
- They are fun to be around. They don’t take themselves too seriously. They smile and joke easily. They have a rich life beyond the office. They have an infectious enthusiasm for the work. They are relentlessly positive. Remember, you might be spending more time with this person than with your significant other. Make sure that time will be enjoyable.
- They don’t freak out when things go wrong. Who wants to work for someone who is constantly in a panic?
- QUOTE:
2016
- http://kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html
- QUOTE:
- Q1. Explain what regularization is and why it is useful.
- Q2. Which data scientists do you admire most? which startups?
- Q3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
- Q4. Explain what precision and measure are. How do they relate to the ROC curve?
- Q5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
- Q6. What is root cause analysis?
- Q7. Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.
- 8. What is statistical power?
- 9. Explain what resampling methods are and why they are useful. Also explain their limitations.
- 10. Is it better to have too many false positives, or too many false negatives? Explain.
- 11. What is selection bias, why is it important and how can you avoid it?
- …
- QUOTE: