On a new team, start with meeting people. This includes your team, stakeholders and cross-functional partners. Ask them about the company, product, team, help they need and seek advice. Understand the career growth plans for every member of your team.
2. Understand product/company:
Read docs. Ask questions (lots of them). Attend cross-functional meetings. Try out the product yourself. Dig deeper to understand goals and success metrics of the products and company. Recommend creating an shared live doc where you invite other folks to add their comments & suggestions.
3. Build out team vision and roadmap:
Document customer pain points. Map that against the projects that your team is executing. Learn about the top successes and misses. Articulate team vision. Build a roadmap. Iterate with partners and get alignment with leadership.
4. Focus on Impact:
Identify projects in the first 90 days that will deliver impact early. Stay focused on long term vision and impact. Keep learning. Get alignment with the leadership on how success will be measured. Roll up your sleeves and start delivering what the team & customers needs most.
As a data scientist, I am not dissatisfied. I love what I do!
But I might have gotten lucky since I got into this for the right reasons. I was looking for a role that had a little bit of both tech & business and so few years back, Business Intelligence and Data Analysis seemed like a great place to start. So I did that for a while. Then industry evolved and the analytics maturity of the companies that I worked also evolved and so worked on building predictive models and became what they now call “Data scientist”.
It doesn’t mean that data science is the right role for everyone.
One of my friends feels that it’s not that “technical” and doesn’t like this role. He is more than happy with data engineer role where he gets to build stuff and dive deeper into technologies.
One of my other friends doesn’t like that you don’t own business/product outcomes and prefers a product manager role (even though he has worked as a data analyst for a while now and is working on transitioning away).
So, just based on the empirical data that I have, data science might not be an ideal path for everyone.
It depends on your target industry & where they are in their life-cycle.
It has four stages: Startup, Growth, Maturity, Decline.
Generalization is great in earlier stages. If you are targeting jobs at startups; generalize. You should know enough about lot of things.
T-shaped professionals are great for Growth stage. They specialize in something but still know enough about lot of things. E.g. Sr Growth/Marketing Analyst. Know enough about analytics & data science to be dangerous but specializes in marketing.
Specialization is great for mature industries. They know a lot about few things. E.g. Statisticians in an Insurance industry. They have made careers out of building risk models.
I remember Taking a course “Database Management systems” while pursuing Bachelor’s in computer Engineering. I liked it, I liked it a lot and so I thought that It would be a great experience to get some hands on professional experience about Databases and so I went on an Internship search and soon, I found one!
The task was about designing and developing a database for their “materials management project”. They had decided to build an Internal Application that automates their processes and replace their existing “manual” work. I remember once I was given a stack of about 1000 papers to study their existing processes! Also, I also got the chance to talk with the users and “Interview” them for the Task of designing databases. I was guided by Developers in their IT Department who had designed and developed database for some of their existing applications. And It was a nice experience of designing the database for the real-world! After that, I did some database development work and wrote some SQL that I had learned at my “Database Management systems” class at my University. Fun times!