Get Up To Speed Fast As A Junior Data Scientist
You are a new junior data scientist and you want to get started the right way.
You want to make sure you don't make the same mistakes others have made early in their data scientist careers because you want to prove to your employers that they made the right choice. As such, you need to figure out how to get up to speed as fast as possible.
To get started quickly, there three main questions you want to ask when joining a new group as a junior data scientist.
You've got the job and have started as a junior data scientist. To get your bearings as quickly as possible you need to ask three main questions.
1. What are the most important Key Performance Indicators in this domain?
2. What are the most relevant classic case studies in this domain?
3. Who are the industry thought leaders (internal & external) I should be learning from?
Let's look at question 1 - What are the most important Key Performance Indicators in this domain?
While you may have been doing Kaggle competitions, side projects, and working on your portfolio, you are now part of a data scientist team in a large organization. Which means that you work as a team and work within the confines of what is defined as success within the team and the organization. This means that the sooner you understand what your and your team's work is measured against, the better. This helps you understand project prioritization as well as helps you develop your own personal criteria and compass for what you should be working on and what you need to be able to show.
Let's look at question 2 - What are the most relevant classic case studies in this domain?
The industry, the organization, and your team has faced issues in the past that will have been resolved or studied. There is a set of working knowledge that people in your group, company, and other companies will have about what has worked and hasn't worked in the past. Yes, you may be applying new techniques, but it will all be flavored by what has worked and hasn't worked in the past. This means that you want get the historical context for what works, what doesn't work, and what has been tried. This helps you understand decisions managers and senior members of the team will make as well as help you develop your personal approach to data science projects.
Let's look at question 3 - Who are the industry thought leaders (internal & external) I should be learning from?
Within industries and organizations there are thought leaders who are driving the agenda, experiments, knowledge, and how to think about what is going on within the small world they inhabit and lead. Every group and organization will have different people they inherently trust and listen to. You need to know who they are, familiarize their work, and become conversant in their viewpoints, world views, and recommendations. This will help you better understand how your team and organization learns about cutting edge techniques and applications, as well as gives you a natural topic to talk about. This will also help you stay on top of industry news, gossip, firings, hirings, and the softer-side of the industry.
You've just joined a data science team as a junior data scientist and you want to integrate and become effective as soon as possible.
Unfortunately, it's hard to know what matters because you are new to the job and maybe even the industry.
To get your bearings as quickly as possible you need to ask three main questions.
1. What are the most important Key Performance Indicators for our group's work?
2. What are the most relevant classic case studies in this field?
3. Who are the industry thought leaders (internal & external) I should be learning from?
The answers to these questions will help you understand how your group and organization learn, whom they are learning from, what they've learned and experienced in the past, and how decisions are made. Which will help you get up to speed incredibly quick and will help you develop a style of work that perfectly suites the organization and team you have joined as a junior data scientist.
Your next action...
Your next action is to write down your own personal thoughts on what these answers are for your group and organization. Then when you have this list find find a senior data scientist or group head and ask them to sense-check your answers. Hopefully you were right on the answers. If not, then great - a learning opportunity and a great conversation starter.