You won't be a great data scientist... until you do these 3 things
Things you should do before, during, and after a project -- recommendations from a data scientist with over 7 years experience at Amazon...
Hey nerdy friends,
I know I haven't updated for a while, today I have a very import newsletter that will bring your data science career to the next level 🚀.
Here are the 3 things you should do before, during, and after a project.
Before the project: ask how your data science project is going to fit in the bigger picture.
Don't just put your head down once your manager ask you to do an analysis. Ask why they need this analysis.
Why? Because when managers need an analysis to make a business decision, they don't know the details of the limitation of the data. Or, they don't have the data science background to think beyond the very few methods they know.
By letting them explain the big picture, you'll often identify that the analysis they ask for won't solve their problems. It's important to brainstorm with them, and make sure the analysis is not just what they think they need, but what helps them achieve their goal.
Don't treat your data science project like a 'lego piece'.
Think like a business owner, ask why. You want to make sure your analysis is going in the right direction before you dive in the hard work.
Take time to align with stakeholders might seem to be time consuming in the beginning, but it'll save you a lot of headache down the line.
2. During the project: get feedback early.
Data science projects often fail because the data is not good enough to solve the problem, or sometimes it's because the business requirement changes.
When you start a project, don't just have the verbal confirmation, but also have a written documentation of the agreement on the problem you are trying to solve, and what are the goals you try to achieve.
Every time the object changes, make sure it's reflected on paper, so everyone is on the same page.
If you only get feedback from your stakeholder when you finish 50% of it, it might be too late.
Get feedback early.
Your first demo doesn't need to be perfect. It could be as easy as showing them your code and visualization on a Jupyter notebook. Don't need to spend too much time to make a perfect presentation.
The goal is to see whether the stakeholders think it's going in the right direction.
Most people are visual. When you present, have a table or a diagram, not just bullet points. Again, the goal is not to show a pretty graph, but to help other people see your point.
Stake-holder alignment work doesn't just happen in the beginning of the project, you need to keep doing it throughout the project, and never assume that the objective of the project stays the same.
3. After the project: follow up to make sure it's successful and get the measurement of the impact.
I know you hate writing documents after a project, you just want to move on.
But what if your manager asks you details about it 3 months later, can you remember it?
What if you need documents for your promotion, can you provide a concrete report from 'problem statement' to 'model evaluation'?
Just push yourself a little more, block a few days to finish the documentation!
Besides, always have follow-up meetings with the engineering teams/PM teams that use your analysis. Make sure it's successful.
Because sometimes the launch of a data science project can be blocked because of lack of ownership. I know on paper your project is completed, but it's worthwhile to be a leader in the last sprint to make sure it's in production. You might also need to educate them how to use your analysis, and provide support.
It's your job to ensure the data science project generates business impact, instead of staying as an analysis report. Your responsibility doesn't stop at the analysis phase. So, follow through.
After the launch is successful, schedule periodically sync up with the stakeholders to ask for feedback to improve your future analysis.
Ask them about the metrics to measure your impact. Not just the statistical performance, but how much revenue your work generated, how much time you saved, or how much better the decision making process is because of your report.
Those learnings will help you design your next data science project better, and you need to document those metrics for your future performance review before you forget.
To summarize:
Don't treat your data science project as a lego piece. Ask why
Get feedback early from the stakeholders. Iterate.
Follow up after the project is done. Make sure it is useful and collect performance metrics.
Look, the 3 things are not related to your technical skills, but your communication and leadership skills.
I want to share more with you, but I need your help. If I were to create a course to help you go to the next level of your data science career, what do you want to learn from me?
Tell me in this 1-min survey. 😊 I'll really appreciate your feedback and your answer means a lot to me!
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Until next time,
Daliana Liu