Build a Career in Data Science
By Jacqueline Nolis and Emily Robinson
Build a Career in Data ScienceOct 02, 2022
Oops! We're Both Unemployed!!
This very surprise bonus episode was made after Emily and Jacqueline found themselves simultaneously unemployed. Here Emily will chat about being part of a 15% company-wide labor reduction while Jacqueline walks through the steps she's been doing as she interviews with new companies. Join them for some vulnerable conversation that are relevant to this current wave of layoffs.
Interlude: Data Science Subfields
In this special live episode recorded at PyLadies London, Emily and Jacqueline discuss those little subfields of data science like experimentation and fraud. They ponder the benefits of becoming more specialized in your career and how different fields have different cultures. The episode ends with a Q&A using live audience questions!
Epilogue: So What Have We Learned?
In the final episode of Season 1, Emily and Jacqueline take a moment to reflect on all they've learned in writing a book and making a podcast about data science careers.
Interlude: Managing Your Manager
In this special live episode recorded at the Data Science DC, Emily and Jacqueline talk about data scientists' relationships with their managers. They discuss how you should communicate with your manager, how much you should be doing what your manager asks vs what you think is best for the company, and other topics. Several times in the episode Jacqueline and Emily disagree, adding drama and intrigue.
Chapter 16: Moving up the ladder
After you are a senior data scientist for a few years, what is next? In this episode Jacqueline and Emily talk about three possible career moves for seasoned data scientists: becoming a manager, a technical lead, or an independent consultant. They discuss the pros and cons of the different types of jobs and strategies for getting into those roles.
Chapter 15: Leaving Your Job Gracefully
"You can't fire me I quit!" --a great example of not leaving a job gracefully (but hey it happens!). In this week's episode Emily and Jacqueline discuss how to decide when the right moment is for you to leave a job, and what steps you should take. They also talk about the realities of the situation, like managers who don't seem to listen when you discuss why you are unhappy, walking out of job, and getting a last minute counter offer.
Interlude: Help! My Job Search Isn't Working
In this special live episode recorded at the ODSC East conference, Emily and Jacqueline discuss what to think about it you can't seem to find that data science job you want. They talk about the different reasons why job hunts don't work, what you can do about it, and answer listener questions from the audience.
Chapter 14: Joining the Community
Social media, conferences, blogs, and meetups--there are many places where data scientists congregate outside of work. But how does a data scientist become a part of these communities and contribute? In this episode Jacqueline and Emily discuss speaking at conferences, awkwardly attending your first meetup, and many other moments that can happen when a data scientist starts to join the community.
Chapter 13: Data Science Failures
You're not a failure! The field of data science is overflowing with projects that just didn't work out. This episode we talk about these projects and why they fail. We also discuss how to handle them emotionally even though it might feel like you're solely responsible. In the game, Emily makes Jacqueline try and discern true stories of data science mistakes from her twisted deceptions.
Chapter 12: Stakeholders
Stakeholders! The people who data scientists help by creating analyses and models. Stakeholders have so many needs, and keeping them satisfied is one of the most important parts of being a successful data scientist. In this episode Jacqueline and Emily discuss how to make your stakeholders happy with good communication and empathy.
Chapter 11: Data Science in Production
Putting data science into production can mean a ton of things: from customer-facing models run millions of times a day to continuously live dashboards for stakeholders. But writing code for production and getting it to work can be intimidating for many data scientists, and lots of us have never tried. In this episode we talk about production, why it can be scary, and what you can do to get started! We also have a game that just might rip off a much more popular TV show.
Chapter 10: Making an Analysis
An analysis is the work of taking data and turning it into a PowerPoint presentation (more or less), and in this episode we talk all about it. What makes an analysis sharp, how to sell data stories to stakeholders, and the useful tool of an "analysis plan" are all discussed. The episode ends with Emily giving Jacqueline an analysis game that tangentially relates to puppies.
Chapter 9: Starting a New Job
Welcome to your new data science role! You just started a new data science job, maybe your first one, and wow there is a lot to do! In this episode, we cover what to expect in the first few months of the job and what to do if it’s not what you hoped. We also discuss how to handle the inevitable heartbreak as you find technical skeletons in the closet like production databases that are just csv files on a SharePoint.
Season 1 Intermission!
Technically still just in time for the last of the holidays - a special bonus episode! Here we cover three small mini-topics. First, whether you should get a PhD to become a data scientist (spoiler alert: almost certainly not), delving deep into the culture of academia and our own PhD experiences. Then we share how we decide if we should learn a new topic/technology. Finally, we talk about how to design good data science interviews as a hiring manager. Do we advocate for white boarding, take-homes, and unstructured interviews? Listen to hear our hot takes!
Chapter 8: Negotiating the Offer
Stop! Don’t just accept that coveted data science job offer right off the bat. Instead, listen to this episode where we discuss negotiating: what’s on the table, what salaries to expect at different levels of data scientist, what the heck RSUs and stock options are, and much more. By reflecting on what’s important to you and how to ask for it, you’ll start your new job in the best way. Includes stories from Jacqueline and Emily's pasts!
Chapter 7: the Job Interview
The interview: possibly the most nerve-wracking part of a data science job search. In this episode, we’ll help you prepare by covering the full interview process, including the different types of interviews, what questions you might be asked, and what the interviewers are looking for. And since interviewing is a two-way street, we also do a mock interview where Emily as the interviewee tries to see if there are red flags in a few jobs.
Chapter 6: Résumés and Cover Letters
“You only get one chance at a first impression.” When applying for data science jobs, that’s usually your resume (and sometimes a cover letter). So how can you give your data science resume the polish it deserves? In this episode, we discuss what hiring managers and recruiters are looking for in a resume, how and why to customize it for different jobs, and weigh in on the always hotly contested: “how many pages should it be?”
Chapter 5: the Job Search
Before you can get a data science job, you need to find one! In this episode, we discuss how to decode job descriptions, use your network, and find jobs that don’t need 3+ years of experience. We share how we’ve found our previous jobs and we do some on-the-spot judging of job posts that Emily found on LinkedIn.
Chapter 4: Building a Portfolio
Perhaps the most common piece of advice for aspiring data scientists is to make a project portfolio. Despite this, so few data scientists do so! In this episode, we discuss what exactly a portfolio is, the benefits, and the common reasons people don’t do it and how to overcome them. Spoiler: it's just as much psychological as it is about time and skills.
Chapter 3: Getting the Skills
It seems there are so many “required” skills for a data science job—how can someone possibly learn them? In this episode, we discuss four possible ways to do so: a formal degree program, a boot camp, learning on the job, and teaching yourself. We also share our own very different backgrounds: Jacqueline's math master's and engineering PhD versus Emily's statistics minor, master's in organizational behavior, and a boot camp.
Chapter 2: Data Science Companies
While the popular image of a data scientist is one solving cutting-edge problems at a large tech company, data scientists work in every type of organization. In this episode, we talk through five company archetypes, from small start-ups to government contractors to traditional retail companies. We weigh the pros and cons of working at each and debate the career changing question of: "should you be a company's first data scientist?"
Chapter 1: What is Data Science?
What actually *is* data science, and what does a data scientist do? What kind of backgrounds do data scientists come from and what skills do you need to be one? In this episode we start with the basics—declaring once and for all what is data science anyway and exploring how the hype of the field matches reality. We explore the three main areas of data science - analytics, decision science, and machine learning - and help you figure out which is best for YOU.