Building a successful data operation is a serious undertaking, one that crosses nearly all functions. No matter what your data goals are, you’ll need a data science pro with the right skills for your particular project.
How do you find the right data scientist to help answer your most pressing data questions? With the perfect data scientist job description. Read this article from top to bottom for the full picture, or jump to a specific area:
- Part 1—What does a data scientist do?
- Part 2—Questions to ask before writing a data scientist project description
- Part 3—Sample data science project description
- Part 4—Selecting the right data scientist
What does a data scientist do?
Data science uses data to make informed decisions–whether it’s getting to know your customers through user segmentation, improving your website through A/B tests or using predictive modeling to identify new business opportunities.
It comprises an array of fields, from computer science and statistical analysis to machine learning and data visualization. Data scientists often have advanced degrees in mathematics, physics, or computer science, while others have transitioned from statistical and analytical roles. The one trait they all share, however, is the ability to use scientific methods to analyze and verify results.
Here are a few skills to look for:
- Data science and analytics (quantitative analysis, modeling, statistics, etc.)
- Machine learning
- Languages such as R, Python, MatLab, etc.
- Big Data frameworks such as Spark, Hadoop, etc.
- Cloud platforms such as AWS
Questions to ask before writing your data science project description
Before posting a job, consider these questions: Why are you engaging a data scientist? Start by identifying what your problem or goal is, so you can begin to identify the capabilities and skills needed for your project.
You’ll want to start by identifying your data science needs. This will provide data scientists with the context they need to gauge they’ll scope and approach to your project, and the expertise required to get it done.
- What type of data are you analyzing?
- How much of that data do you have? How much will it grow?
- What’s the current state of that data? Is it structured and sorted, or highly unstructured? Do you need algorithms or models designed to help you wrangle your data?
- What do you need from the data? Is this mission-critical, highly sensitive, or more informational?
- What tools are you currently using? You’ll also want to make sure they’re familiar with the tools they’re going to be using on your project, whether those are statistical languages like R or Python, or database technologies like Hadoop.
- What do you hope to learn from that data? By including your goal in the project description, professionals can better understand the type of work that’s required.
- Would you benefit from someone with highly specialized skills in a few areas of data science, or would a well-rounded expert serve you better?
- Are there any time constraints to consider with this project? Let professionals know of any milestones or deadlines that could impact the timeline.
- What kind of budget will this project have? The more experienced and expertise a data scientist has the higher their rate.
- Industry experience. Many data science professionals will have prior experience in various fields of business that can help them better understand your specific goals and requirements.
Sample data scientist project description
Keep in mind that many people use the term “job description,” but a full job description is only needed for employees. When engaging a freelancer as an independent contractor, you typically just need a statement of work, job post, or any other document that describes the work to be done.
Your job post needs to answer three questions:
- What do you need to be done?
- When do you need it?
- What are the start and end dates for your project?
Answering these questions with the pertinent details can not only help you attract more qualified talent but potential partners will be able to assess whether they’re a good match. You’ll also receive more accurate proposals.
Did you notice the unusual item under requirements? Yes, asking to include the word “shrimp” in the cover letter. Adding things like this is optional, but when you need someone detailed, it’s a good way to see if they’re observant and follow instructions well. It’s also a quick way to filter through a pile of proposals
Selecting the right data scientist
Once you receive a few qualified proposals, be sure to use an interview to learn more about the data scientist’s approach to the problem, their experience, and how they’ve used creativity and talent to accomplish similar goals in the past. Prepare questions ahead of time, such as:
- “Tell me about three data science projects you’ve worked on?” Ask about their most similar projects, favorite projects, or most recent. Listen for how they solved the initial problem, challenges that came up during the process, and what they did to address them.
- “What’s your production timeline?” Get more details about how quickly they work, how much time they’ve spent on previous data projects, and how they receive and implement feedback.
- “What makes a great [insert type of data project here]?” Learn more about how they’ll approach your project as well as their experience with similar work.
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