[Checklist] Screening Data Scientists vs. Analysts vs. Engineers
This is part of our hiring checklist series, where we share insights to help you screen developers. You can find our other checklists here:
If you’ve been tasked with recruiting for roles within data teams, you may think to yourself, “What’s the difference between Data Analysts, Data Scientists, and Data Engineers?”
While the titles may sound similar, each role actually focuses on a different aspect of data utilization, all equally necessary in helping organizations use data to make better business decisions.
At their best, data-driven teams’ skill sets complement one another, facilitating the capture, interpretation, and dissemination of huge amounts of relevant data. Together, they help companies become more data-driven with quantitative evidence – and they rely on one another to do it.
The nuances between Data Analysts, Data Scientists, Data Engineers may seem minute at first, but each has a distinct role to play in deriving and conveying meaningful insights from data. Here’s a great overview on what to expect from each respective role.
A Data Analyst is a generalist, tasked with interpreting an eclectic range of data to inform business decisions. They bridge the gap between the technical and non-technical, spending as much time cleaning and analyzing data as they do creating explanatory visuals and descriptive reports.
This role is instrumental in helping dissect the quantitative ramifications of key business decisions. At their core, they play the role of a technical interpreter, relaying technical insights in a way that resonates with the company’s non-technical stakeholders.
Here’s what to search for:
- Do they have a basic understanding of statistics and a firm grounding in basic data analysis principles (e.g. relevant experience through coursework or previous positions and projects in an “Analyst” role of some kind?)
- Do they have a track record of helping companies make effective decisions through data, with quantifiable success (whether as a Business Analyst, Operations Analyst, Business Intelligence Analyst, or through similar roles or projects)?
- Are they familiar with your analytics stack of choice? Or are they familiar with tools/methods that would help them pick it up quickly?
- Do they have hands-on experience with exploratory data analysis and data visualization? (e.g. personal projects, school projects, work experience, internships)
- Do they have a background in the programs most frequently utilized by your data science team (e.g. SQL, Python, R, and so on)?
- Are they an effective verbal, written, and visual communicator, with the ability to interpret and explain complex technical topics to a non-technical audience?
- Are they an attentive listener, capable of turning ambiguous non-technical requests into actionable technical tasks with little guidance?
- Do they possess excellent business acumen, allowing them to peacefully mediate the needs of various stakeholders with aplomb?
- Are they self-motivated, and capable of problem-solving independently, with minimal oversight or guidance? When they get stuck, do they rely on others for help or do they take the initiative to figure things out themselves?
- Do they exhibit an innate curiosity and a willingness to dig deep across datasets to provide thoughtful and relevant intel?
- Do they possess a strong sense of creativity that allows them to analyze and interpret data through numerous business lenses?
- Do they have natural business savvy and an understanding of the company’s business model? Can they think like a member of your C-Suite?
- Are they open-minded and willing to follow the story the data exhibits? Can they regularly put aside their opinions on the business to provide truly objective analysis?
A Data Scientist is an expert in math and statistics who uses data to make intelligent business predictions and continually improves the way the company uses data.
Like Data Analysts, they’re tasked with answering core business questions through the power of data. That said, unlike a Data Analyst, they’re also expected to utilize their expertise in algorithms, machine learning, statistics, and other quantitative fields to anticipate core business questions and needs before they arise. They don’t just interpret data; they help to optimize its usage across the company.
Though they’re generally more specialized than their Analyst counterparts (often possessing a secondary degree in a quantitative field), they span a variety of seniority and experience levels. Be sure you clearly align with your hiring manager on the seniority level they need before starting your search.
These are key characteristics to search for:
- Do they have experience bringing order to large sets of disorganized datasets, both structured and unstructured?
- Do they have an in-depth understanding of general statistics, applied statistics, and/or machine learning, verified by previous projects, work experience, or coursework?
- Have they previously worked in a capacity where they were able to maintain and/or train machine learning models, with quantifiable successes for the business?
- Do they have an extensive background in the programs most frequently utilized by your data science team (e.g. SQL, Python, R, and so on)?
- Do they have a history of successful collaboration across a data-driven team, including Data Analysts, Data Engineers, and fellow Data Scientists?
- Do they have an eye for detail that will allow them to catch inconsistencies and inefficiencies in the data, promoting continual improvement of the architecture they work in?
- Can they advocate for their needs in the context of a team? When they encounter architectural issues, can they convey and insist on the changes they need?
- Are they effective communicators, with the ability to effectively convey their findings across a variety of channels (written, verbal, and visual)? Can they do this for both technical and more non-technical audiences?
- Do they possess basic business savvy that will allow them to train models in a way that produces meaningful, impactful data insights for the company?
- Are they curious, probing, and even skeptical by nature? Do they depend on their investigative skills to evaluate and design solutions, or do they tend to take assumptions at face value?
- Are they willing to experiment and iterate on how to use data effectively across the company?
- Are they objective in the workplace, allowing them to dissect key analytical questions in a pragmatic way?
- Do they demonstrate strong storytelling skills, with the ability to showcase their findings in a way that’s both meaningful and compelling?
- Do they prioritize thinking about the deep ethical questions around using data to influence decisions?
Data Engineers make both Data Analysts’ and Data Scientists’ jobs possible. With a much heavier focus on software development, Data Engineers build and manage the architectures that capture the data Data Analysts and Data Scientists use. If they’re not building or managing data pipelines, they’re maintaining databases and large-scale processing systems.
Data Engineers are effective generalists with a background in both software development and data science. Since they’re tasked with maintaining the environment that both Data Analysts and Data Scientists work in, it’s important that they’re not only technically effective, but team-oriented; their job has a huge impact on the roles of others, both positively and negatively.
Here’s where to start:
- Do they come from a “generalist” background in software development, with the ability to comfortably switch between and combine technologies to achieve an overarching goal?
- Are they familiar with the needs of a data-driven team and the architectural groundwork necessary to allow Data Analysts and Data Scientists to thrive?
- Do they have an extensive background in one or more of the frameworks utilized by your data engineering team (e.g. Hadoop, NoSQL, Spark, Python, and so on)?
- Do they have proven experience promoting data accessibility, efficiency, and quality within an organization?
- Are they responsive and empathetic to the needs of their teammates, especially when it comes to requests for optimizations and other architectural changes?
- Are they receptive to constructive feedback and suggestions from their teammates? Do they implement the feedback they’ve received or are they cemented in their ways?
- Are they familiar with your team’s development methodology of choice (e.g. agile, scrum, spiral, and so on)? Or are they familiar with a similar framework that would help them to pick it up quickly?
- Are they collaborative and team-oriented? Are they as focused on the needs of their teammates as they are on their own?
- Do they show an interest in continual self-development within their area of expertise, or do they prefer to stick to the methods and concepts they already know?
- Are they objective and willing to adjust their methods to promote team-wide success, instead of their own personal preferences?
- Are they focused and self-motivated? Can they manage their work proactively with little to no oversight?
- Do they have advanced technical and non-technical communications skills that they can utilize to interpret and implement their teammates’ requests?
Stay aligned with your technical teams
What’s your evaluation process when searching for the roles above? How do you determine which skills your team is most focused on in their next hire? Tell us your best practices in the comments.
NEXT: Read our checklist for assessing DevOps talent here.