Previously, we discussed the various roles in data science (data scientist, applied scientist, research scientist, machine learning engineer) and read about a data scientist who found himself mainly doing non-data science, SQL monkey work.
Now, you might be thinking, how can we prevent ourselves from getting into a role that’s not what we expect? Or if we’re in such a situation, what can we do?
The first line of defense is to read the job description carefully. Don’t just focus on the job title! In this (slightly dated) study, researchers found that job seekers mostly paid attention to the job title and company, neglecting other information such as job requirements. We should spend time examining the requirements and job scope too. Most of the time, hiring managers and recruiters make an effort to customize these to match the role.
If we land an interview, we should ask the right questions. It’s as much about us assessing them as them assessing us. Here are some useful questions to suss out the role:
More about the goals, skills, tools, and deliverables of the various roles here.
If you’re fairly certain about the role (and maybe have an offer), contact future team members you’ve met—during the interview cycle—to learn more. Or look them up via LinkedIn. In addition to the questions above, ask about the culture and your future boss. Usually, peers don’t have as much of an incentive to “sell” you the role and tend to provide more objective information.
We can also read reviews on sites such as Glassdoor. This can be useful for organizations with a single, centralized, data team. Nonetheless, note that in large organizations with multiple data teams, each team might be structured differently and have different cultures. Use your judgment and take these reviews with a pinch of salt.
For example, you might be building machine learning models, putting them into production, and working on engineering and devops tasks. However, you have the title of data scientist while peers (in other organizations) have titles such as applied scientist or machine learning engineer.
Depending on how you define data scientist, your title may not reflect the work you do. But I think it’s not that bad. At least you’re on the right side of the problem; it’s way better than having the right title but work on the wrong tasks (like this guy).
Don’t let your title define you. (Your job should not define you either for that matter.) If you’re doing the right work and growing towards your aspirations, just keep at it—this is what matters. In interviews, people care about the work you’ve done and what you can do, not your title.
The Internet doesn’t care about your title.— Naval (@naval) November 9, 2020
For example, you might have joined a team expecting to build and deploy ML models. Instead, you find most of your time spent on foundational data engineering work and providing analysis for business decisions.
First, assess if the situation is temporary or permanent. If we joined a start-up with nascent data capabilities, the initial data engineering and ad hoc analysis might be unavoidable (and hopefully, temporary). On the other hand, if we joined Facebook as a (non-core) data scientist, the core work of data extraction, analysis, and statistics is likely to be permanent.
Assuming it’s only temporary, I think it’s healthy to embrace it and enjoy the journey. Hey, at least we get to work with data. It also stretches us outside of our comfort zone, on tasks we don’t usually work on (e.g., data engineering, infra)—this is a great learning opportunity. (The situation could be far worse; imagine being conned into a company that does not have data…)
At the same time, work with your boss to carve out time for a 20%/research project. It should match organizational needs and your aspirations. Such projects can directly improve revenue, cost, or customer experience, or build team capabilities (e.g., internal library for rapid experimentation). If the project goes well, you could be spearheading a new initiative and get to write your ideal job description.
What if there’s zero opportunity to work on such projects? Well, it’s not the end. Most of us work 40 - 50 hours a week; there’s plenty of personal time for self-learning and projects. And public data is widely available. Personal projects are a great way to learn new techniques and gain hands-on practice. It’s also easier to share the code and write about our process, making it a solid addition to our portfolios.
Catfishing is the act of deliberately presenting false or misleading information to fool someone. While originally done in the context of social media, it could happen in the hiring process too, where the recruiter or hiring manager portrays the actual role in a misleading manner.
If you find yourself in this situation, what can you do? First, remain calm. It could be a misunderstanding due to disorganization from HR and/or the hiring manager. Also, you might not get the full picture from the first few days at work, especially if you’re working remotely.
As soon as you can, find time to talk to your manager to get on the same page. Were the expectations shared during the hiring process accurate? Talk to your peers (if you have peers) to learn more about the role. Give yourself some time to investigate.
Once you have sufficient information, decide if the situation can be salvaged. If the situation is only temporary and everything else is awesome, give it some time to work itself out. However, if you decide that you can’t accept it and have to move on, try reaching out to the other offers you received and explain the situation. With any luck, you might be able to accept a previous offer.
This is similar to the situation of right role, wrong title. Having a less prestigious title can hurt the ego. Nonetheless, try to push through and focus on the learning and impact of the new job. Let your growth, work, and results speak for you; don’t use the title as a crutch.
Depending on the new company, you can try negotiating the job title with your boss and HR. Success largely depends on the existing organizational structure. If the title is fairly standard (e.g., data scientist) and several others have the same title, it might be a hard sell. (The hiring manager might have a tough time explaining the title difference to existing team members, or have to rebrand everyone). But if the role is fairly new and HR is flexible, changing your title might be possible.
If you need to invoke your academic pedigree or job title for people to believe what you say, then you need a better argument. - Neil deGrasse Tyson
Do the due diligence before accepting the job offer; this is the best way to prevent a role or title mismatch. Ask the right questions, contact future teammates, read the job reviews.
If you find yourself in the wrong role, assess if it’s temporary and how much is within your control. Focus on what you can control (it’s healthier this way). Reframe your mindset, find opportunities for 20% time projects, and do self-learning and projects in your free time.
Overall, if you’re doing meaningful, high impact work, learning lots, and have great peers, don’t let the title weigh you down. It’s just a title—your competencies, results, and job satisfaction matter much more.
Last week, we discussed roles in data science & ML.— Eugene Yan (@eugeneyan) November 18, 2020
This week, we discuss:
• How not to get into the wrong role
• What if we have the right role, but wrong title
• What if we have the wrong role
• What if we're changing jobs, but to a "lesser" title https://t.co/lporgUPN8j
Thanks to Yang Xinyi for reading drafts of this.
I write about how to be effective in data science, learning, and career. Get weekly updates.
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