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⏳ Questions Students Ask

Outside of my normal 9-to-5 job, I also teach a class at my alma mater of the University of Washington. Having done this for a few years, I often get similar questions from my students in the classroom. My class tends towards people who are early or mid career and are attempting to either pivot into the field of data science, or become more skilled/literate with data analysis and use that to level up there already extant subject-matter expertise. I've captured some of those questions below and expanded on them more than I normally do in class.

💼 What do you look for when hiring? What do you recommend for building your first data portfolio pieces?

I have mostly done hiring at smaller organizations, so it tends to be higher-touch experience than at a large tech company, and you can use that to your advantage. In scenarios like this, if you can craft your portfolio examples tailored specifically for the role you're applying to, it really goes a long way. For example, when trying to get into my last job, I did a very niche environmental science data visualization for folks and that demonstrated that--while I didn't have deep expertise in the domain--I could quickly become conversant in new ideas that I would be introduced to.

In general, there are a handful of core things I look for when hiring:

  1. End-to-end work (going from research question/business case -> implementation in code/infra -> analytical work -> output summary, which may or may not include visualization) demonstrates ability to work in many different DS roles.
  2. A domain of interest/curiosity that hasn't been done to death (i.e. NOT titanic random forest or iris k-means) shows you're a creative thinker and can approach problems from different vantage points, plus the subject matter is a differentiator unto itself.
  3. Different levels of accessibility--maybe you summarize everything into a nice blog post and walk through your analytical process, but then you share your source on GitHub in a well-documented manner. This demonstrates communication & collaboration skills as well--namely, knowing your audience. The best candidates have at least 2 examples like this on their personal site, but possibly more. Typically I weight an ongoing programming or data analysis project higher than I would a one-off, because it demonstrates consistent engagement with an idea over time, but I don't discount anybody for having tons of small projects. Hopefully that's helpful; I can expand or provide examples on any of these ideas.

🔎 How do you recommend tailoring your job search for the first time, especially coming from a discipline that is not focused on technology?

  1. Be kind to yourself. - You’re trying to switch careers into a new domain and also learn the technical aspects of the domain and also learn the new professional communication tactics, rhythms, and customs. Each of these things separately is a full time job. It will absolutely take time to get you there. It’s really helpful to have conversations with other people who are doing similar work, support each other, and keep in touch about learning and growing. This is what networking really is—not bullshit glad-handing. I can add more context on this if helpful/necessary/desired, but honest, engaged human connection on the job search is powerful and like rocket fuel in some situations.
  2. Job hunting is like writing--just sit in the chair and do it. Block off an hour every day to look for jobs. Use affinity organizations (communities based around software you use in this domain, professional groups, etc. ), LinkedIn, and job boards to find jobs. Apply on the company website if possible, rather than through an aggregator. Save those jobs in some form (text doc or spreadsheet on your computer is okay), review them, and apply. If applying via Linkedin, bias toward companies where you have a 2nd or 3rd degree contact, and try to connect with them and ask for a referral. If you can make friends with a trustworthy recruiter, interview them for what they’re looking for. Use the context across all of these to find more jobs. Set some goal for yourself—personally, I keep going until I find five jobs I’m confident about (>60% responsibility, duties, or skill match) and apply to each of them.
  3. Learn to notice. Part of the work you have to do right now is understanding how filtering works to determine good jobs for you vs. bad ones. At this point, avoid the term “senior” and optimize for “junior” or jobs without a qualification at the beginning (senior may have management duties and would be less focused on technical expertise, but would still require domain knowledge, so you probably wouldn’t be well-suited). As we talked about, there’s not necessarily a lot of permanent openings for just a “Taxonomist” or “Ontologist”, but there may be some positions for “Metadata Specialist”, “Semantic Web Knowledge Manager”, or “Solutions Architect” that do essentially the same work. Read the descriptions closely and understand what what the firm is looking for as best you can—keep track of words and noun phrases that reoccur and try to do research into them if they’re unfamiliar. Consider contract positions that are small and scoped tasks or consulting—I can provide more feedback and guidance on this if it’s of interest.
  4. Build in reps as fast as you can. Try to get your first conversation with a hiring manager or recruiter as early in this process as possible. Ask for feedback when you can (some orgs like Amazon don’t give feedback). Some recruiters, being time poor, will end a conversation with you early if you’re not a good fit and tell you why, which is definitely to your advantage as rejections go. All the information you can gather here is a scientific experiment—what are the different things you can say to represent yourself or convey your experience to get a step further through the job-seeking process? Build in psychological distance so you can stay true to point 1! Half the battle is positioning; the other half is doing the work. You will fail, but you want to fail fast and fail often so you can learn faster & reposition.
  5. Augment on your supplemental materials. While they won’t explicitly get you a job, they’re invaluable if you’re going to attempt to transition into the space from a field you don’t have professional experience in. If you can do an end-to-end project which captures use of your taxonomy/ontology in some way, that will both level you up re: domain knowledge (pizza taxonomy -> ML classifier; based on pizza metadata, assign category -> explanation of results) but also act as an artifact that supports your candidacy. It’ll give you something to talk about.

📕 How do I find the best resources to continue learning or professional development outside the classroom?

When I worked at the University of North Carolina helping faculty members with course design, one professor did something I liked a lot. She presented the fundamental elements of each unit to students as required readings, and then paired them with a section called "If You Want to Know More". In this section, there were materials designed to augment the narrative threads that often came up in class or in the other reading materials.

Across the board I highly recommend books by Allen B. Downey. His materials were a major force multiplier in my ability to learn and understand programming, after coming from a non-STEM background.

Programming, Computational Thinking, and Systems Thinking

The MSIM program's curriculum is a great generalist program, but unless you seek out these materials, you won't explicitly learn any of these data elements.

Data Visualization, Narrative, and Communication

Data Ethics & Humanistic Approaches to Data