I recently landed a new Data Science job 🎉 and while I worked with an awesome recruiter to land this role, my experience will probably differ from yours, especially if you’re just starting out
Regardless, if you’re looking for a job in Data Science, I’ll outline the steps I take to get ready for new roles.
Update Resume and LinkedIn
You'll want to update your LinkedIn summary with keywords about your past work to get the recruiter’s attention. Edit your responsibilities at your last role with metrics.
Update Twitter bio and name
I like to throw a briefcase in my Twitter name followed by “is looking for work” or something catchy to remind folks when they see anything you tweet. If you can include your past employers or languages you know in your bio. Always include a link to your blog or personal site to give recruiters more info about you before reaching out.
Network at Meetups
If you don’t already have them it’s a great time to make or update your business cards and hit the road. Attend your local data meetups and don’t let anyone forget you’re looking for a new job. This is the time to talk about your work and it’s likely someone will be hiring.
Data Science Roadmap 🗺
Okay, so you got the job, what now?
First comes the typical onboarding paperwork, but after that, it’s often unclear the steps you can take to get set up for success and set yourself apart from other newbies.
While I both hate and embrace the feeling of being new to an org I think I can shed some light on steps I’ve taken to ramp up.
1) Create a Company Knowledge Base ☁️
When I start a new role I like to create a new page in Notion (my go-to note-taking tool) and start filling it in with pages to track my work. I suggest doing this as soon as you know your start date and create pages for the Job Description, Questions you have along the way, a meeting database, and a to-do Kanban. Starting this from day one makes sure you have your notes squared away and shows you have your ducks in a row.
2) Suggest Improvements to Onboarding 🧐
Your company hired you for your big juicy brain so get some small wins early by providing good feedback on their onboarding process. What did you like, what didn’t you like? What could be smoother and what aspects confused you? Giving your company knowledge about how their onboarding process might be difficult or
3) Maintain Inbox Zero 🗑
I didn’t use to think it mattered if I let my emails pile up, but it’s so much easier to find what you need later down the line when you just smash the archive button when you read a new
4) Take Notes 📝
I mean it, take notes about everything! Especially during standups if you want data to come back and say “Hey Greg, in one of our standups last week you said you were having issues getting the server CPU down. I’ve worked on that in the past, maybe I can help.” Don’t zone out in meetings even if you aren’t sure what someone is talking about. Write it down and make it an opportunity to ask them about it later.
6) Create a Morning Routine 💼
I typically come in, make a cup of coffee, check my email, and read a paper or article before our daily standup. Right after standup, I make a list of things I need to get done that day. This sense of structure helps my day and start on the right foot.
7) Show your Coworkers you Care 🗝
This doesn’t have to be by bribing them with sugar like donuts and candy, but let's say you read a cool article related to a problem your team is facing. Share that in your Slack/team communication channel. Remember, your job is to add value.
8) Save Everything 💾
I use Google Chrome bookmarks to save, not just my most commonly used links, but I have folders for Papers related to my work, Documentation, code examples, and Stack Overflow answers. Banking these about the questions you have early on will give you something to reference. If you find a code snippet, SAVE it.
Data Science Comes in all Sizes 🧮
A realization I’ve currently had is how large and how stratified your DS organization is has a larger impact on your work than the problem you’re solving. I’ve had experience at an early stage and high growth startups, as well as medium-large tech companies, with teams ranging from just myself to a department of over 40.
There are so many other ways your department can operate. If you’re at an internal service organization you can expect to get tickets or tasks from other departments, collaborate closely with the business, and have your own “customers” who you provide insights for. If you’re at a product-driven organization the models you create become part of products so the focus is more on deployment, model management, and model risk reduction.
Another important factor is how new Data Science is to your organization. In the grand scheme of things we can think about most companies in two groups, those who create ML/AI tools as part of their main product and those who don’t. Those who don’t may move slower and their willingness to become a data company may not be as high. At companies where ML/AI is the product, data science is usually seen as a small slice of their analytics/engineering department. The reason this matters is how much support, advocacy, and pushback you might get on a day-to-day basis. When you’re looking for new roles you should always ask how long the company has been doing data science and how much of a priority it is to executives.
This ties into how the expectations of roles in Data Science don’t match up to reality. We want to make a huge impact at our organization and a large one in the world, but unless ML is your company’s core business you may feel like you aren’t “changing the world.”
It’s impossible to list all of the ways you can be situated at a Data Science organization, so I’ll highlight some of the challenges you can face at departments of different sizes.
Xtra Small (1 Data Scientist)
If you’re the only data scientist in an organization you can expect to do most of the heavy lifting when it comes to project design, direction, and steering ideas. You’ll probably have only one data engineer if at all so you need to be comfortable performing your own ETL and data cleaning. Strong project management skills are important as you’ll be in control of the process from beginning to end. These roles are good for strong Data Scientists with a lot of experience because the unknown unknowns are large for those early in their career.
If you’ve got a few folks to help you out, you can expect to work closely with them to talk through modeling, pair program with, and outline priorities. You’ll want to have a good working relationship on your team and understand your relative roles. Some people may be more averse to speaking to execs and stakeholders while some want to step up in that role. You may have goals assigned by leaders in Engineering or Analytics that you work closely with your team to divvy up.
Smedium (5 - 10)
With some close comrades, it’s easier to take a more specialized role at your company. At Smedium DS departments you may see people start to break into specializations like analysts, ML engineers, and NLP or Computer Vision specialists. At this size, it becomes easier to follow some of your interests where it aligns with company goals. If you’re highly interested in ML in embedded systems, you may have more leeway to do more embedded projects while still fulfilling the main responsibilities of your role.
Mid Size (10-20)
At this size, you may see some smaller teams emerge within your team. Some organizations break their DS departments up by both job function and type of data they work on. Others form product teams that can include people with different sorts of data roles. You may also see data engineering or data warehousing split out as their department grows to sustain analytical growth. One of the biggest challenges here is finding your role in the department. New hires should to find gaps in the groups’ knowledge and fill them.
Departments of this size tend to leverage project managers and full data engineering teams to support analytics. What’s nice is you may have more direction and resources at the start of new projects, but sometimes work can just feel like you’re doing only assigned tasks. Here you want to separate yourself as a specialist or a high-performer on your team. Leverage that each team member is probably more versed in one topic and set up time to learn from them one on one. You have a huge opportunity to help other folks on your team with data cleaning or take charge of new project ideas without worrying about abandoning priorities.
At a large Data Science department, you can expect data or models to be part of the product or the company as a whole is large, a la Google. Here you can expect your work to be more siloed to the product team you’re on or you may have few projects outside your domain (ml modeling, data engineering, etc). These roles are good because you have a lot more resources available and you may have more mentors or mentors with more time to devote to helping you grow. You may only work with one subtype of data, or you may be customer-facing and sell the product with your knowledge of what it’s capabilities are.
My Favorite Things 🌟
Check out this AMAZING Github repo of Explainable AI resources. If you are a practitioner or just want to learn this repo has papers, books, and software tools to try and create more interpretable machine learning algorithms.
Check out one of my recent favorite episodes, Algorithmic Injustices and Relational Ethics with Abeba Birhane.
While not released yet, I’ve read portions from this data science career book and it’s been a great read so far. Look out of this when it comes out at the end of this month!
As a precaution, the Women in Data Science Event in San Diego has transformed into a one-day virtual conference to avoid spreading the COVID—19 virus. Tickets are still on sale for the live-streamed conference. We have confirmed speakers from companies like Adobe, Square, and Seismic Software.