Hire Data Scientist: All You Should Know

Hire Data Scientist: All You Should Know

Published on
June 27, 2022
Updated on
September 26, 2022
Author
Luan Campos
Reading Time
We'll cover what a data scientist does, how they can help you reach your marketing goals, and how you can hire one.

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If you need in-depth analyses of market and industry trends, consider hiring a data scientist.

As seasoned data wranglers, data scientists are analytical experts who use their social science and technology skills to analyze, model, transform, and manage large sets of unstructured and structured data. They then use the results to create actionable business plans for your company.

However, hiring the right data scientist can be an uphill battle. While there's a glut of data scientists on LinkedIn and other job sites, many lack the experience and skills to translate findings into understandable reports.

That's why we've written this comprehensive guide about why and how you can hire data scientists. We'll cover what a data scientist does, how they can help you reach your marketing goals, and how you can hire one. We'll also provide tips for writing top-notch data scientist job descriptions and interview questions.

What Is a Data Scientist?

Data scientists are tech-savvy analytical experts who bring new perspectives to business development and marketing.

Data science professionals use their unique blend of business acumen and mathematical skills to extract, clean, and deploy data-driven solutions for businesses. Many companies use data scientists to find, manage, and analyze large amounts of company, industry, and customer data.

What Does a Data Scientist Do?

A data scientist's responsibilities vary depending on their company. However, most companies require data scientists to:

  • Collect large amounts of unstructured and structured data
  • Research and create statistical models for data analysis
  • Transform data into more readable and usable formats
  • Look for patterns and trends in data to help their company's bottom line
  • Implement data-driven insights
  • Stay on top of analytical techniques such as deep learning, ML, and TA
  • Communicate and collaborate with business and IT departments
  • Collaborate with engineering and product design departments to develop and enhance offerings

Data scientists typically use the following technologies to analyze trends and produce reports:

  • Data visualization tools: By representing data as graphs, charts, and maps, data visualization tools provide an accessible way to understand data trends, outliers, and patterns.
  • Machine learning (ML): ML is a branch of artificial intelligence (AI) based on automation and algorithms. Many data scientists use ML to automate data analysis and make real-time predictions without human intervention.
  • Pattern recognition: This involves automated recognition of regularities and patterns in data. Pattern recognition has applications in image analysis, statistical data analysis, information retrieval, data compression, and ML.
  • Text analytics (TA): An ML technique used to extract key insights from unstructured text data, TA features many processes, including text cleaning, word frequency calculation, and removing stopwords. Many data scientists use TA to monitor real-time feedback on social media sites like Twitter and Facebook.
  • Deep learning: Part of a broader family of ML methods, deep learning unearths insights and trends through artificial neural networks. Deep learning mimics human thought by clustering data and making predictions.

Why Should You Hire a Data Scientist?

Hiring the right data scientist can help you identify business opportunities, test decisions, and much more. Here are the main reasons you should hire a data scientist:

  • Define organizational goals: Data scientists can examine and analyze your company data to improve performance and align business goals. This, in turn, can help you engage customers better and increase Return on Investment (ROI).
  • Identify opportunities: Data scientists will look at industry trends and company data to locate new products and services you can offer. They can also unearth potential partnership, marketing, and other business opportunities.
  • Test previous decisions: Interested in analyzing the consequences of a decision you made a few months ago? A skilled data scientist can look at your past decisions to see how they've affected your organization. This will help you plan future decisions better.
  • Identify target audiences: Most businesses have at least one source of customer data. Data scientists can use this data to identify the demographics of your customer base, such as age, location, and gender. Your marketers can then use this data to personalize products and services for different customer groups.
  • Help management make better decisions: Experienced data scientists can be trusted advisors to upper management. By demonstrating the value of the data that they've collected and analyzed, they can accelerate and refine the decision-making process.
  • Help company staff adopt analytics tools: If you're seeking to implement analytics tools throughout your organization, a data scientist can teach the staff how to derive meaningful insights from the tools. Once the staff understands how to use the tools, they can use these insights to address business challenges.
  • Find the best candidates for the company: Last but not least, data scientists can help you source and hire candidates for open roles. They can scour social media, job sites, and company databases to locate the best hires for your team. They can also use their data mining skills to simultaneously process thousands of resumes and choose the best candidates to interview.

How To Hire Data Scientists

As you can see, there are many reasons to hire data scientists. However, before you start hiring, you need to know whether your company is ready for a data scientist. You also need to consider potential candidates' skills, qualifications, experience levels, and salaries.

Check Whether Your Company Is Ready for a Data Scientist

First, you need to have the right datasets, company culture, and recruitment specialists in place. Otherwise, there's a high chance of recruiting the wrong person for the job.

Data

Ask yourself whether you have enormous data sets that create a ton of questions with no obvious solutions. For example, you may have such datasets if:

  • You have a wide range of products
  • You have a lot of historical data about your company and you'd like to learn more about how to future-proof your company
  • You have thousands of customers talking about your brand every hour

If you have these datasets, a data scientist could help you locate vital insights about your company. Skilled data scientists will perform statistical interpretation, build robust recommendation engines, and deploy test predictive models using your large datasets to predict and analyze current and future trends.

On the other hand, if you have small datasets and you only want to want to create a few graphs to understand trends, you're not ready for a data scientist. Instead, consider hiring a data analyst. Data analysts can provide the insight your company needs and maintain data warehousing services to accommodate data sets.

Company Culture

Next, you need to evaluate your company culture.

Data scientists are extremely curious. Many of them aren't satisfied with just being consultants — they want to conduct thorough research using your datasets. As such, you need to give them room to grow and allow them to create new data solutions and tools. You may also have to give them access to different departments and services. If your company culture doesn't give them enough room to grow, your hires will eventually get frustrated and leave.

Here are some tips from McKinsey for developing a data scientist-friendly culture:

  1. Approach data analysis as a way to make better decisions: Don't approach data science as an exercise in gathering and analyzing data for data's sake. The goal of collecting, dissecting, and deploying data is to make better business decisions. As Rob Casper, chief data officer at JPMorgan Chase says, "Volume is not a viable strategy." Instead, the most important objective is to locate business problems and direct data management efforts toward solving them.
  2. Commitment from C-suite executives is vital: To retain talent and provide more opportunities for growth, C-suite executives need to implement and show data scientists how important their contributions are. They should sit down with data scientists, listen to what they have to say, and share feedback.
  3. Stimulate demand for data: Get your staff excited about data and data science by integrating them into workshops, seminars, and other company events. However, keep in mind that creating beautiful graphs and buying new ML tools doesn't cut it. You need to organically stimulate demand for data from the bottom up to bake data science into your company culture. You can do this by encouraging data scientists to educate their colleagues about data and data science.

Recruitment Specialists

Last but not least, you need to know whether you have the right recruitment specialists in place. If your Human Resources (HR) department has little to no experience hiring data scientists, you may not be able to hire skilled data science professionals for your team.

Remember, data scientists enjoy a sizzling hot job market. According to Glassdoor, the average U.S.-based data scientist makes a whopping $117,212 per year, with additional cash compensation averaging $13,989. As such, you have to compete against cutting-edge companies with global influence and name recognition.

Make a List of Essential Data Scientist Skills and Qualifications

Once you have the right datasets, company culture, and recruitment specialists, you need to make a list of essential skills for potential hires. These include:

Hard Skills

The ideal data scientist should have the following hard or technical skills:

Data Preparation

Data preparation involves getting data ready for analysis, including data transformation, discovery, and cleaning tasks. It's a key part of the analytics workflow for data scientists and analysts alike.

Specifically, your hire should be able to use data prep tools like Tableau Prep Builder to:

  • Find, arrange, gather, model, and process data
  • Analyze large volumes of unstructured and structured data
  • Prepare and show data in the best forms for problem-solving and decision-making
Programming Skills

Your hire should boast robust programming skills. Although they don't have to build and execute apps or sites, data scientists need to use complex systems to analyze and process data. As such, they need to know how to write maintainable and efficient code in at least five of the following languages:

  • Python: A general-purpose coding language, Python can be used for implementing algorithms and data processing. It can also be used to train ML and deep learning algorithms.
  • JavaScript: Closely associated with applications and web development, JavaScript can be used to create interactive web pages and apps. Data scientists should know how to use JavaScript to create visualizations.
  • Java: Not to be confused with JavaScript, Java is a programming language typically used for Android apps, desktop applications, credit card programming, and web apps. Data scientists typically use Java for data science frameworks and big data tools such as Hadoop and Apache Spark.
  • Scala: An improved extension of Java, Scala enables sleek frameworks for handling siloed data, making it a great fit for enterprise-wide data science. It's also highly scalable and functional due to vast libraries and support for synchronized and concurrent processing.
  • Julia: A specialized coding language, Julia is made for numerical analysis and computations. Your data scientist should know Julia if they're going to be focusing on deep learning, data visualization, interactive computing, or numerical analysis. Julia is fast enough for interactive computing but can switch to a low-level coding language as needed.
  • Structured query language (SQL): SQL is a natural choice for manipulating structured data and relational databases. As a querying language, it allows data scientists to locate, adjust, and check massive datasets.
  • R: A specialized language that's great for intuitive visualizations and statistical analysis, R is designed to handle complex processing and massive data sets. It has a statistics-oriented syntax and offers powerful visualizations of results.
  • MATLAB: A programming environment and language specific to statistical and mathematical computing, MATLAB offers built-in tools for dynamic visualizations and a deep learning toolbox.
Math and Statistics Skills

Like programming, mathematics and statistics play critical roles in data science. Data scientists must know how to use mathematical and statistical models and apply and expand on them. They also need to know how to:

  • Perform data analysis and spot important relationships and patterns
  • Understand the limitations and strengths of different test models
  • Design new solutions by merging or modifying pre-existing tools and techniques
  • Apply statistical thinking to separate signal from noise
AI and ML Skills

Your data scientist should know how to train and use ML and AI models. This will enable them to spot and analyze patterns faster. They should also be able to explain ML and AI predictions to other departments as needed.

Soft Skills

Besides well-honed hard skills, the best data scientist for your company should also have soft or non-technical skills like communication skills, critical thinking skills, and business acumen. These skills don't require training or certification, but they play a large role in the application of data science to the workplace. Without these skills, your hire won't be able to work with the rest of your team to analyze and implement data-derived business insights.

Effective Communication Skills

Like other IT professionals, your data scientist should have effective communication skills. Whether they're entry-level or senior level, they need to know how to connect with other people and departments. They also need to know when to listen and speak and how to clearly explain their findings to non-technical and technical audiences.

Critical Thinking Skills

Data scientists must have strong critical thinking skills to pinpoint appropriate questions and understand how results relate to your company's goals. They also need critical thinking skills to see all angles of a problem before forming opinions.

Business Acumen

To create actionable insights for your business, data scientists need to have sharp business acumen. In particular, they need to:

  • Understand the special needs of your business and industry
  • Translate data into results that work for your company
  • Know what problems need to be solved and why
  • Translate data insights into results that work for your company
  • Consider how data insights can support future growth and success
Curiosity

A data scientist must be highly curious. They must have the drive to create, find, and answer questions about your business, industry, and consumers. The right hire will never stop asking questions — they will constantly be on the lookout for questions and answers, and the ones they find won't stop them from seeking more.

Proactive Problem Solving

Finally, your data science professional should know how to solve problems proactively. They should be able to do the following before problems crop up:

  • Spot opportunities and explain solutions
  • Identify existing resources and assumptions before approaching problems
  • Pinpoint, explain, and implement the most efficient methods to get the best answers

How Much Do Data Scientists Make?

After creating a list of hard and soft skills for prospective hires, you need to think about salaries. A data scientist's salary varies depending on skill level, so let's take a look at the skills and expected salaries of entry-level, junior, and senior data scientists.

Entry-Level Data Scientist Skills and Salary

Entry-level or beginner data scientists have zero to three years of experience. Most are fresh grads with bachelor's degrees in Data Science, Statistics, Computer Science, Mathematics, or other related fields. Some may be self-taught or recent graduates of boot camps.

Because these data scientists have little to no relevant work experience, they tend to have fewer skills and lower salaries. Consider sharpening their skills by providing them with educational resources and mentorship.

According to ZipRecruiter, the average U.S.-based entry-level data scientist makes $68,054 per year.

Entry-level data scientists should have the following skills:

  • Proficiency in Python, including familiarity with Pandas, Scikit-Learn, TensorFlow, and PyTorch
  • Solid knowledge of ML and statistical analysis
  • Strong multi-tasking and time management skills
  • Experience with querying data with SQL

Junior Level Data Scientist Skills and Salary

Junior data scientists have more than three years of experience. Unlike entry-level data scientists, they have proven real-life experience with data science. As such, you can expect more from them.

According to ZipRecruiter, the average U.S.-based junior data scientist earns $70,772 annually.

Specific skills you should look for include:

  • Over three years of experience with Python, Scikit-learn, and Pandas
  • Proven ability and experience in solving business problems through data and analytics
  • Experience in ML techniques, including Bayesian models, random forests, linear and logistic regression, clustering, forecasting, and neural networks
  • Experience with Git version control
  • Experience working in a specific development framework or frameworks, such as Agile, Kanban, or Waterfall
  • Proven ability to explain complex information to staff members
  • Strong business acumen

Senior Data Scientist Skills and Salary

Finally, we have senior data scientists. With four to seven years of professional experience, these IT professionals should know how to:

  • Proactively identify and take on projects that solve complex problems
  • Work closely with engineering, product, and other business leaders to influence program and product decisions with data
  • Apply fundamental and specialized data science methods to drive improvements to your business
  • Design and implement data pipelines
  • Influence leadership to make better data-informed decisions
  • Define and advance best practices in product and data science teams
  • Build actionable and scalable data products, dashboards, key performance indicators (KPIs), and deep dives

The average U.S.-based senior data scientist has a salary of $142,258 per year.

Learn More: Data engineer vs. data scientist: What’s the difference?

Write a Compelling Data Scientist Job Description

The next step is to write compelling data scientist job description to attract top-notch talent. Make sure your job post covers the following at a minimum:

  • Job title
  • Company description
  • Duties and responsibilities
  • Required skills and experience
  • Compensation and benefits
  • Working location and schedule

Here's what your data scientist job description could look like:

Senior Data Scientist — Revelo

Revelo is looking for a fully remote Senior Data Scientist to join our team.

Our hire will be joining an international and highly collaborative team. If you're in Los Angeles (where our U.S. head office is), you can choose to work in a hybrid mode.

This role is open to Senior Data Scientists in the following time zones:

  • Pacific Standard Time (PST)
  • Central Standard Time (CST)
  • Mountain Standard Time (MST)
  • Eastern Standard Time (EST)

Duties:

  • Lead the development of ML and statistical models, including model testing, monitoring, research, debugging, maintenance, and documentation
  • Collaborate with designers, engineers, product managers, and C-suite executives to provide product and service impact and deliver value to customers
  • Proactively identify areas of the business where ML and AI can make the most valuable impact
  • Establish efficient and scalable automated processes for data analysis and ingestion
  • Present business intelligence and advise senior leadership and end-users on model outputs
  • Advise and consults with other departments and teams looking to leverage AI/ML
  • Provide quantitative research and analysis to spot areas for improvement
  • Create dashboards and reports in Looker
  • Implement self-serve reporting in LookML

Requirements:

  • Four to seven years of commercial experience as a Data Scientist solving business problems
  • Experience with quantitative modeling and applied statistics, including survival analysis, experimentation, ML, and regression
  • Strong communication skills
  • Curiosity about problems and analytical approaches
  • Proven track record translating analytical insights into recommendations and clearly communicating them to non-technical and technical stakeholders
  • Extensive experience with Python, SQL, R, Scala, and software engineering fundamentals
  • Comfortable developing reporting in business intelligence tools like Tableau and Looker

Nice-to-have skills:

  • Thorough understanding of advanced SQL techniques
  • Deep understanding of scaling extract, transform, load (ETL) pipelines and dimensional modeling
  • Extensive experience in Software as a Service (SaaS), eCommerce, and marketing
  • Proven experience launching ML and AI models at scale

Compensation:

  • Competitive base salary of $130,000 to $150,000 depending on experience
  • Bonus pay
  • Dental and medical insurance
  • Paid career development courses
  • Four weeks of paid vacation
  • Parental leave

Schedule:

  • 8:30 AM to 5:30 PM PST
  • Monday to Friday

Create Data Scientist Interview Questions

Finally, you need to create interview questions for data scientists.

Most companies ask depersonalized academic questions like "What is data science?" and "What is the difference between deep learning and ML?" These questions can show you how well a prospective hire knows their stuff, but they don't reveal much about your hire's work ethic, experience, and personality.

To get a fuller idea of who your potential hire is and what they can bring to your team, you should ask more personalized questions, such as:

  • What drew you to data science?
  • Have you worked on a data science project that required ML and AI? What was your experience working with ML and AI models?
  • How do you treat outlier values?
  • How do you judge product or service performance?
  • How do you differentiate between bad and good data visualization?
  • What are your best professional qualities? What are your weaknesses?
  • Provide a specific example of when you failed to meet a goal. What went wrong?
  • How did you find your last job?
  • What unique characteristics or skills do you bring that would help our team?
  • How do you handle stress?
  • How do you spot and solve problems for companies?
  • How would you rate your business acumen? What companies have you worked for and what have colleagues said about your business acumen?
  • How would you describe your ideal work environment?
  • Do you have a preferred software development framework, such as DevOps, Kanban, or Agile?
  • What do you like the most about working on a multi-disciplinary team?
  • What's your approach to conflict resolution?
  • How do you explain complex technical challenges or problems to clients and colleagues from non-technical backgrounds? Give an example.
  • Provide a specific example of using data to improve the experience of a stakeholder or customer.

Start Recruiting Data Scientists With Revelo

Finding the right data scientist for your team can be challenging. Luckily, there's Revelo. We'll help you source, hire, and manage data scientists all on one platform. All of our developers have been pre-vetted for their English proficiency, skills, and knowledge.

Contact us today to learn more.

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Our remote engineers are in US time zones or adjacent. Not 10+ hours ahead. Enjoy real-time collaboration with your hires.

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Full-Stack developer with 6 years of experience working with MERN stack. Worked for multiple transnational companies focused on web and cloud, technology services, and R&D. Major professional experience in Frontend with Javascript, Node.js, React.js, and Vue.js

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