Data Science
How to hire data scientists
Knowledge is power, but only if you can extract it from your data. Data scientists help businesses process data and glean insights that can be used to improve their products and services and better reach their target markets.
So how do you find data science consultants? What follows are some tips on how you can find top data scientists on Upwork.
How to shortlist data science professionals
As you’re browsing available data science consultants, it can be helpful to develop a shortlist of the freelancers you may want to interview. You can screen profiles on criteria such as:
- Professionalism. Who’s tailored their submission to speak to your business and your project? Check out their Upwork profile: How do they present themselves in general?
- Talent. Because data scientists are part engineers, part statisticians, and part computer scientists, it should be clear from their proposal, profile, and portfolio that they have the specific mix of skills needed to do the work.
- Experience. Has the freelancer worked for others in your industry, or handled projects like yours in the past?
- Feedback. What do previous clients have to say about the work on their work? Reviewing feedback can give you insight into their ability to communicate, solve problems, and produce a great product.
- Portfolio. Have they handled complex data projects before? What were the results? Each sample should provide a description, which may explain the problem they needed to solve and other project requirements. Make sure they can explain how the work in their portfolio relates to the questions you’re trying to answer.
How to write an effective data science job post
First, define what you want and the skills needed to accomplish those goals. From there, consider further breaking down the project into the specific technologies your data science pro will need to know. Use the information in your brief to create a detailed job post that will attract the type of freelancer you’re looking for. Start by defining a scope of work that focuses on three things:
- Results: What deliverable(s) do you expect?
- Targets: What are your deadlines?
- Time: What are the start and end dates for your project?
You’ll also want to highlight the specific skills you’re looking for. Data science encompasses an array of fields, from computer science to statistical analysis to machine learning to data visualization. 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.
Ready to harness the power of data for your business or organization? Log in and post your data science job on Upwork today.
DATA SCIENTISTS FAQ
What is data science?
Whether it’s using A/B testing to choose between two designs, measuring the performance of your marketing channels by collecting the right KPIs, or data mining the competition to gain new insights on how you can approach your target market, data scientists get you the intel you need to make informed business decisions.
Here’s a quick overview of the skills you should look for in data science professionals:
- Data science and analytics (e.g., quantitative analysis, modeling, statistics)
- Machine learning
- Languages such as R, Python, and MATLAB
- Big data frameworks such as Spark and Hadoop
- Cloud platforms such as AWS
Data scientists help businesses leverage data to get the answers they need to succeed.
Why hire data scientists?
There’s a good deal of confusion and ambiguity around the term data scientist. A data scientist can actually describe several distinct specialties, which we’ll outline here.
- A data analyst is someone who spends most of their time querying databases. They’re often the most junior level of data scientist, and may or may not have much experience with statistical analysis or algorithms. They’re best suited to answering ad hoc questions that can be answered by pulling data from Excel or SQL databases. They should also be able to produce basic visualizations with tools like Tableau as needed. If you already have a well-constructed database and just need someone to answer specific questions, a data analyst might be right for you.
- A data engineer is less engaged in running specific queries than in building the systems to help provide the answers. Unlike data analysts, data engineers often work directly with developers, ensuring that data is properly being captured and stored by the relevant systems. They also ensure that scheduled processing jobs run on schedule. They are most likely to need expertise with big data frameworks like Hadoop and Spark, as well as knowledge of production-oriented programming languages like Java and Python.
- A data scientist needs to be able to oversee complex data projects from beginning to execution. In addition to having great technical skills, they need to be able to effectively communicate their findings to others in the organization. They should also be able to manage a team. They should be able to query databases like an analyst, but also able to perform much more sophisticated analysis using statistical techniques and machine learning, depending on the task at hand.
So, before you go any further, ask yourself: What problem am I trying to solve? A data scientist can help guide your organization’s decision-making process with data. Here are some common questions a data scientist might be able to help you answer:
- How do we improve user retention?
- Who are our most valuable customers?
- How can we decrease turnover among our employees?
- What new features should we prioritize?
As you can see, these questions get right to the core of your business goals. Once you’ve settled on a question or set of questions, you can start getting into more specifics. What data will you need to answer these questions? Are you collecting that data at present? What metrics will you use to measure success?
What kind of data scientist you’re looking for will depend on a couple of things: What kind of question are you trying to answer, what the current state of your data operation is, and what technologies is your team currently using.
A lot will also depend on your current data setup. Does answering your question require you to start collecting data that you haven’t before? If so, you’ll need a data engineer to work with your dev team to make sure trackers are properly set up and that the data being collected is going to the right place. If you don’t already have a steady data pipeline, this can be a significant engineering outlay by itself. Once you’re collecting the data you need, how much will it need to be processed? If your data is messy (meaning it needs to be reformatted or otherwise transformed before it can be used) this adds another layer of complexity.
At present, the programming language Scala and the big data framework Spark are extremely valuable. According to the Stack Overflow Developer Survey, data scientists who use Scala, Spark, and Hadoop command the highest rates in the field, while those who use R, Java, and Python typically charge somewhat less.
How much does it cost to hire a data scientist?
Data science is a hot field, and qualified data scientists can charge more than other kinds of developers or business analysts. On Upwork, rates charged by freelance data scientists can range from $36 to $200 an hour with an average project cost of around $400. Keep in mind, however, that these rates go up when more specific skills, like Scala and Spark, are taken into account. That said, it may make more sense to negotiate a project fee based on the scope of your project.
Remember, aside from ad hoc queries, most data projects are long-term commitments. You’ll need someone who’s familiar with your systems and can help you measure the impact your decisions have made over time. For this reason you may also consider a retainer arrangement. Below we’ve put together a table of some common data science projects, along with some relevant skills and hourly rate ranges charged by some data scientists.
| Type of Project | Relevant Skills | Hourly Rate |
| Analysis and ad hoc queries | Excel, SQL, visualization | $20-$100 |
| Set up a data pipeline | Java, Python, Scala, Hadoop, Spark, data cleaning, | $25-$100 |
| Build a recommendation engine | Statistical analysis, Machine learning, Python, Scala, Spark | $50-$210 |
What’s the difference between a data scientist and data analyst?
Data scientists and data analysts aren’t interchangeable, but they do both have a common goal: to draw insights from data. While their skills will overlap (in many ways, data scientists are advanced analysts), generally data scientists will have a broader and deeper skill set, especially when it comes to their business acumen. They’ll have technical knowledge an analyst won’t necessarily need on a day-to-day basis, such as deep familiarity with Hadoop, advanced statistical modeling, artificial intelligence and machine learning.
Both professionals can transform data into answers business owners need to make better decisions, but what they’re starting with and the skills required to reach those answers will vary. Data analysts can answer your business questions, but data scientists can help you formulate new questions to drive the business forward. And when it comes to complexity, chances are you’ll need a data scientist.
Let’s quickly look at what each does.
Data analysts
Data analysts take known data and glean actionable insights and answers to specific questions you have about your data. These are the pros who funnel insights from data into industries like education, healthcare, and travel to help businesses like airlines and hospitals run better, and deliver better service to customers.
Their value lies in their ability to make data (for example, data that’s been input into a CRM or exported from Google Analytics) more usable for you and your company. Generally, an analyst will
- Clean and sort data
- Uncover new patterns and correlations
- Find actionable insights and package them up for business use
- Use visualizations and interactive dashboards to present findings
- Query data to meet specific needs
- Create reports for key stakeholders
When it comes to unstructured data, analysts may work with a data scientist or data engineer to get help pulling new data sets for analysis.
Data Scientists
Why are most data scientists able to charge almost double the rate or salary as a data analyst? Data scientists have a broader and deeper skill set, especially when it comes to their business acumen. These pros create algorithms and models businesses use to predict future sales, make critical decisions, or launch products. They’re able to do more with more difficult data, including
- Mining large amounts of structured or unstructured data
- Data warehousing
- Advanced programming, with R, SQL, Python, MatLab, and SAS
- Statistical modeling
- Develop machine learning and predictive analytics models
- Work with the Hadoop ecosystem, including Hive and Pig
- Formulating important business questions and hypotheses, then testing validity with math and statistics
A big difference is their ability to work with more complex, unstructured data—as in, data your company either doesn’t currently understand or can’t work with because it’s from multiple disconnected sources. If an analyst is primarily working with your “known data,” a data scientist is equipped to work with any of your company’s data that isn’t known or currently understood.
When a business is making a critical decision, data scientists play a key role. They test theories and hypotheses, the results of which become eye-opening insights key stakeholders can use to make predict outcomes and make more informed decisions.
How to interview a data scientist?
During the interview, be sure you take the opportunity 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 your interview questions ahead of time so you can feel confident that you’ve covered all relevant points. We’ve created a list of data science interview questions you can reference, but here are some additional questions to consider:
- “What do you think of our existing product? What’s something we could be doing that we’re not right now?” Tailor this to ask about something related to your data project—i.e. your existing data pipeline or algorithms—or to learn whether they’ve done their homework.
- “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 project here]?” Learn more about how they’ll approach your project as well as their experience with similar work.