Chapter 92 Data Science Job Descriptions

In this lesson we’ll discuss what data scientists do, what skills companies expect entry-level data scientists to have, and walk through a few data scientist job descriptions.

92.0.1 What Data Scientists Do

Very generally, it’s expected that data scientists know how to interpret and extract meaning from data. But, in practice, what does this actually mean? Well, at a company that sells a product on the Internet, it could the job of a data scientist to figure out what changes to the website caused increases in sales. Or at a company that hires many individuals it could be a data scientist’s job to determine what characteristics of their best hire shave ultimately lead to the biggest increase in revenue for that company. In fact, at Airbnb, they have three different tracks for data scientists to cater to individuals’ strengths and the company’s needs. While the problems differ from one company to the next regardless of location, data scientists work with data to solve problems.

92.0.2 Skills for entry-level positions

We’ll look at examples of job descriptions for data scientist positions below; however, before we get there, we’ll discuss the three general categories of skills that entry-level applicants are expected to have. Note that having all of these skills would be ideal, but not every candidate will be an expert in each and every one. So, on your application, play up your strengths and in your free time be sure to improve upon the skills where you’re less-expert to make your application even stronger over time!

Companies are looking for data scientists to have strong technical skills, that will be good employees, and who have business skills. We’ll walk through what skills fall under each of these categories in this section, but if you’re interested to see others’ thoughts on this, feel free to check out this thread on Quora.

92.0.2.1 Technical Skills

No data scientist will be hired without technical skills. Thus, it’s important to be technically skilled before applying for jobs. The most-common required skills are the following:

Basic Programming Languages : R, Python, and SQL are three of the most common programming languages used by data scientists.
Data Wrangling : Data Scientists spend a great deal of time getting data into a usable format, cleaning the data, and ensuring that the data they have are the data they need. This skill is essential for all data scientists.
Data Visualization : Visualizing data effectively for communication is an important skill for data scientists. Statistics : Having a basic understanding of statistics is often expected of data scientists.
Machine Learning : Machine Learning is frequently used for predictive analyses. Having a basic understanding of machine learning and familiarizing yourself with various approaches will help you get a job.

To demonstrate that you have these technical skills, you’ll want to include them on your resume. However, you should not list every machine learning algorithm you’ve ever heard of. Anyone can Google and list a bunch of algorithms. Instead, simply include “machine learning” as a skill on your resume if it’s one you possess. Then, let your projects and experience do the explaining. By this we mean, the projects you’ve worked on and experience you’ve listed on your resume should demonstrate how you’ve used machine learning to solve a problem. Additionally, having a Project Gallery and GitHub here are critical. The company can go to your GitHub and/or website to see how you’ve applied your technical skills to answer interesting questions.

92.0.2.2 Work Principles

In addition to technical skills, companies will be looking for data scientists who will make good employees. Thus, there are a number of work principles that hiring managers will be looking for.

Good data scientists are dedicated to their work and determined to succeed. No company wants to hire someone, train them, and then lose them right away. As such, companies will be looking for individuals who are dedicated to their job.

Good employees are also dependable. The company must be able to count on you showing up and completing assigned projects.

Finally, data scientists in particular are always learning. Technologies change. Data Formats are altered. New approaches are developed. Thus, data scientists must demonstrate that they are able to learn new things and are adaptable. Being set in one’s ways is not really an option for a data scientist, as the field is always changing and constantly moving forward. It makes for an exciting job, but is not for individuals who struggle to adapt.

These principles should not just be listed on your resume. Again, anyone can write these words in a list. Rather, these qualities should be demonstrated on your resume through your experience and come through in the stories you tell in your cover letter. Examples are the way to demonstrate that you’ll be a good employee.

92.0.2.3 Business Skills

Finally, data scientists very rarely work in isolation. Instead, they’re part of a team. This means that as a data scientist you’ll likely be working with other data scientists and other individuals at the company. Thus, it’s incredibly important that data scientists possess a number of business skills:

effective communicators
problem solvers
knowledgeable
curious & interested

It’s expected that data scientists be able to talk with others to help hone the question that they’re trying to ask and to effectively communicate the results of their analyses. Similarly, to do the job of a data scientist, it requires one to solve problems in creative and new ways. Being a problem solver who is knowledgeable about how to work with data and the data the company has is an important skill. Finally, it will be expected that you’re curious to learn more and interested in what you’re doing.

These qualities can also come through on your resume, website, and cover letter through examples. Additionally, in an interview (which we’ll discuss in a later lesson), it’s important that those interviewing you see that you possess these qualities.

Data Science Skills

92.0.3 Job Descriptions

Now that we’ve discussed the general qualities and skills expected of data scientists, let’s get down to actually discussing what you’ll see in data science job descriptions.

92.0.3.1 General Job Description

In the following lesson, we’ll discuss where to find open jobs, but for now we’ll just make sure we’re all on the same page about what you will generally find in job descriptions! Typically, a job description will have a few components:

Logo - the company’s logo will typically be at the top of the job description
Job Title - the job title of the open position
Location - where this job is located
Introduction - Brief introduction to the company and position
Job Description - this section will describe the responsibilities of the posted position as well as some specifics on the required and expected skills a successful applicant would possess
Job Qualifications - this section will bullet point out the educational, knowledge, abilities, skills, and experience requirements they’re looking for in the individual who fills this position
Preferred Qualifications - Sometimes, a company will choose to include some optional skills or abilities they’d like from a candidate, but that aren’t required.

General Job Description

Now that we have a general understanding of what information is included in a job description, let’s walk through five actual job postings from Airbnb, Claire’s, and Allstate to see what information each company is looking for in a “Data Scientist”

92.0.3.2 Data Science at Airbnb

At Airbnb, there are three tracks for data science positions. The Analytics Track, the Algorithms Track, and the Inference Track. Very briefly here, data scientists on the:

analytics track focus on monitoring metrics of the company, building tools, and helping the company make data-driven decisions.
algorithms track are responsible for building and interpreting algorithms that will help to power data products at the company
inference track use statistical techniques to determine causal relationships

For each of these three categories, we’ll first walk through a job description from Airbnb. We’ll break the description down by what technical, employee, and business skills they company is looking for in each position. After the Airbnb examples, we’ll walk through two more examples of a data science job descriptions from other companies and do the same. This way, you’ll have a fairly complete picture of what job descriptions look like and what skills companies are looking for from their data scientists.

The way we’ll break down each post is by first highlighting what technical skills the company is looking for in green. We’ll then highlight the type of employee they’re looking for in purple. Finally, we’ll highlight the business skills they’re seeking in orange.

92.0.3.3 Airbnb: Analytics

At Airbnb, data scientists on the analytics team have a number of responsibilities and are particularly skilled at asking really good questions, automating analyses and visualizing data. Data scientists at Airbnb on the analytics team are responsible for providing the company with recommendations that drive changes to how things at Airbnb are done.

Here we have an excerpt from a job posting at Airbnb for a data science - analytics position:

Analytics

As mentioned earlier, data scientist positions will always require technical skills of the applicant. In this job posting we see a number of technical skill requirements, including programming, statistics, and data visualization skills.

Technical Skills - Analytics

Additionally, Airbnb expects that the individuals they hire will be good employees. As such, they have to take ownership of their work, be able to strategize with others, and be open to learning new things.

Work Principles - Analytics

Finally, there are a number of business skills that a data scientist must possess. In this job description we see that Airbnb’s data science analytics team really prioritizes the ability to communicate their findings effectively.

Business skills - Analytics

92.0.3.4 Airbnb: Algorithms

Data scientists on the algorithms track are responsible for understanding and working with different types of data, developing machine learning algorithms, and both producing and managing data products to help the company’s users’ experiences.

In their job description they explain a bit about the position and then include a number of sample projects to give prospective applicants the type of work they would do in this position as well as list a number of qualifications of the position.

Among these are a number of technical skills. For this position, applicants are expected to have a deep understanding of machine learning, statistics, and programming at scale. The strongest candidates will also have experience in natural language processing (NLP).

Technical Skills - Algorithms

This particular job description explicitly states fewer work principles than the analyst job description, but does state that the ideal candidates will be team players and open to leadership roles. That said, while not explicitly stated, it’s still implied that anyone they hire will be dedicated, dependable, and interested in their work.

Work Principles - Algorithms

Finally, the successful applicant will be able to take a project, manage it, and move it from start to finish. This is an important quality and expected skill of data scientist on the algorithm track at Airbnb.

Business Skills - Algorithms

92.0.3.5 Airbnb: Inference

Data scientists on the inference track are responsible for generating hypotheses, carrying out experiments that will help determine causality, and refine strategies to drive decisions within the company.

For this position (as with the last two positions), the job description states a number of desired technical skills, including programming, data wrangling and statistical knowledge.

Technical Skills - Inference

Additionally, this job description states that successful applicants will be focused, detail-oriented, team players who are passionate about their work.

Work Principles - Inference

Finally, the job description explicitly states that written and verbal communication skills are valued for individuals in this position.

Business Skills - Inference

92.0.3.6 Airbnb job descriptions

Across all three job descriptions, there are technical skills, work principles, and business skills stated directly in the job description.

While the technical skills vary from one position to the next, the expectations of good employees, and business skills expected from employees overlap a great deal across positions. It’s expected that all data scientists and Airbnb will be accountable for their work and able to take a project from start to finish successfully. These qualities help make individuals good employees. Additionally, on the business front, communication skills, the ability to work on teams, and being a successful problem solver are expectations of all data scientists.

92.0.3.7 Claire’s

However, data scientists are being hired at companies across the world. Airbnb is certainly not the only company. Thus, here we’ll look at a job description for a “Junior Data Scientist” at Claire’s.

For this position, the job description starts by explaining that the person in this position is responsible for using data to make decisions on “promotions, space planning, competitive landscape and ad hoc analyses.”

The description then lists “Responsibilities”, “Qualifications + Experience”, and “Preferred Qualifications.” Among these are a number of required technical skills, including programming, statistics, and data visualization.

Technical Skills - Claire’s

Beyond technical skills, this position is looking for an individual who is a strong team member and who is willing to teach others.

Work Principles - Claire’s

Finally, this data scientist would be able to communicate their findings via reports that could be understood by partners and business users.

Business Skills - Claire’s

Here, while a different company than Airbnb, in this data scientist job posting at Claire’s, we can see that technical skills, work principles, and business skills are all still required for this position.

92.0.3.8 Allstate

For one final example, we’ll look at a job posting from Allstate. The job posting for this “Data Scientist” position begins by explaining the benefits of working in the insurance field and describes the impact this individual would have.

After this, the key responsibilities and job qualifications are listed. Among these, many technical skills are required, including statistical and machine learning knowledge and experience, programming skills, and the ability to work with large datasets.

Technical Skills - Allstate

For work principles, this job description is looking for an individual will to teach others, to be flexible in and capable of learning new technologies, and to be dependable.

Work Principles - Allstate

Finally, Allstate is looking for someone who is able to communicate written and oral data-centric information effectively and to present well to others.

Business Skills - Allstate

92.0.4 Which Job is Right For You?

Of the job descriptions above, which job is right for you? Well, at this point, if your skills are limited to what’s been taught in this course, you aren’t yet fully-versed in machine learning. Thus, the analytics position at Airbnb or the junior data scientist position at Claire’s may be the best fits.

As you continue your education and garner more experience in machine learning or artificial intelligence, you can work toward acquiring the skills required by the other data scientist positions.

Thus, looking for jobs now (which we’ll discuss in detail in the next lesson!) that fit your current qualifications will likely mean looking for “data analytics” or “data science” positions that don’t focus on machine learning, but more work can help you move into those other positions!

92.0.5 Summary

In this lesson we’ve discussed the basic parts of job descriptions and walked through five actual data science job descriptions. We’ve demonstrated that having technical skills, adhering to important work principles, and having solid business skills (such as being an effective communicator) are required for every position. The details of which technical skills are required differs from one position to the next, so it’s ideal to apply to positions that best fit your current skill set. Then, with experience and more training, you’ll increase your skills and open the doors to additional positions.

Summary

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