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This post is contributed by Linda Burtch, Burtch Works’ Managing Director with 30+ years’ experience in quantitative recruiting

As our latest hiring market research shows, 82% of data science and analytics teams are hiring this year, and many of them are not just backfilling due to attrition – they’re adding to headcount.

Although the recent interest in these fields has led to an increase in analytical talent at the junior level, hiring for senior-level quantitative positions, especially those Director and Executive positions, taps a much smaller pool of talent.

 

Writing Job Descriptions for Data Science & Analytics Leadership Positions

I’ve written before about my tips for hiring data science and analytics talent, but I thought it might be prudent to share some advice about another tricky area: how to write compelling job descriptions for senior leadership positions. A job description will likely be your first chance to make an impression on a potential candidate, so crafting a strong description is important.

I’ve reviewed hundreds of senior-level job descriptions over the years, and also been privy to conversations with candidates considering these roles. So, I thought I would share my insights on what elements make a job description compelling, the ideal format, and some examples to guide your efforts.

 

4 Areas of Focus for Data Science & Analytics Job Descriptions

Section #1: Talk About Your Company

A company description should focus on several areas. Although the candidate may be familiar with your organization already, quickly outlining your company’s values and mission statement can give them a better feel for your priorities and address their preconceived notions about your company. And, if they aren’t already familiar with you, this is your chance to make a positive introduction.

Areas to address:
  • Your business (who you are and what you do)
  • Company culture
  • Mission statement/company values

This section should be a short paragraph, to the point, and no longer than 3-4 sentences.

Example: We are a fast growing retail organization specializing in providing the highest quality service to our customers, and we believe that starts with the employees we hire. We look for curious, driven professionals with a will to innovate. We are committed to investing in our talent and providing opportunities for growth for those that share our vision.

 

Section #2: Brief Overview/Summary of the Position

The second section should be a general overview of what the position entails, what it will focus on, and any relevant background information. Save the specifics regarding responsibilities and qualifications for later sections, and focus on the bigger picture.

 Areas to address:
  • Job title
  • Who or where the role reports to (i.e. CEO, VP of Analytics, etc.)
  • General description of the role’s purpose
  • What teams or people the role will interface with (i.e. Marketing, Sales, Finance, etc.)
  • History of the role (why it was created)
  • Other factors to consider (are they expected to be a thought leader or analytics evangelist?)
  • How much of the role will focus on the data management aspect

This section should be a short paragraph and no longer than 5 sentences.

Example: The VP of Analytics will oversee the organization’s advanced analytics group, which provides analyses and insights to multiple business units across the company. This position will work closely with company leaders to better understand and assess strategies to grow the business or optimize processes through smarter use of data.

Section #3: Responsibilities

Here is where you can drill down into daily and overarching responsibilities for the position. While there will be some overlap with the overview here, this section should be more specific, quantified if necessary, and lay out expectations for what you want the person to accomplish and how.

Areas to address:
  • How many direct reports are there and how big is the team as a whole? Is growth expected?
  • Who is this person partnering with and on what? (Finance, Operations, Sales)
  • What are the goals and objectives of the team they’ll be leading?
  • How is success in this role measured?
  • Are there quantifiable targets they’ll be expected to hit?
  • What are the specific tools and methodologies they’ll be using?
  • What is the current data environment? Data structure? Condition of the data?
  • Is there a hands-on component? How much hands-on work is expected?

 

This section can be a bulleted list, but should be listed in order of importance and focus on specifics.

Example:

  • Provide direction and leadership for the development and evaluation of advanced analytics models
  • Communicate across organizations to understand current business needs and determine how to best leverage the data to meet those needs
  • Interface with the Data Management Team to assess data needs
  • Manage a team of 10 data scientists

 

Section #4: Requirements and Qualifications

Finally, the last section should focus on what experience and methods are required or ideal for success in this role.

Areas to address:
  • Number of years’ experience (broken down by leadership or tool, if necessary)
  • Education (degree attained and area of study)
  • Tools (Python, SAS, R, etc.)
  • Techniques (focus on use cases, do you need specialized expertise?)
  • Data ecosystems (Hadoop, cloud, etc.)
  • Data mining tools (Hive, etc.)
  • Types of databases (Oracle, etc.)
  • Industry background (Financial Services, Marketing or Advertising, etc.)
  • Domain knowledge (such as pricing, promotions/loyalty, etc.)

 

This section can also be a bulleted list, should be listed in order of importance and focus on specifics. Keep in mind that you may not find a “unicorn” candidate who checks every single box!

Example:

  • 10-15 years of experience in the analytics field with 5+ years managing a quantitative team
  • Master’s degree in a quantitative field required, PhD preferred
  • Experienced in Python or R statistical programming
  • Experience with Hadoop ecosystem
  • Hospitality or retail industry experience
  • Capable communicator able to effectively present to non-technical audiences

 

It’s no secret that data science and analytics leaders have more options now than they used to. With increased opportunities comes increased scrutiny from candidates considering potential roles, so selling the opportunity – its importance, impact, and scope – is a key aspect of a successful job description.

Hopefully this outline and the examples provided will form a good starting point for employers looking to hire senior leadership talent, and, of course, I’m always happy to chat if you’d like a recruiter’s perspective on hiring for your role!

We hope this information was helpful! If you’re looking to hire predictive analytics or data science talent, or are looking for new opportunities, be sure to connect with me on LinkedIn.

Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.

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Want highlights from our 2019 Data Science & Analytics report, including salaries, demographic comparisons, and hiring market analysis? Watch our 15-minute RECAP video below!

 

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