In this blog, we will discuss how to hire data scientists with the perfect skillset for your business.
Data science can be thought of as the job of “turning raw data into understanding, insight, and knowledge” (Wickham & Grolemund, 2016). Data analysts, statisticians, and quantitative analysts are similar roles, with emphasis on different skills.
Job description of a data scientist and/or data engineer
You will often come across the terms “data scientists” and “data engineers” being used interchangeably. Their job involves deriving insights from diverse and vast datasets. The insights they derive help businesses to make better decisions.
Data scientist uses data mining techniques. Their work involves statistical analysis, furthermore, they might need to build effective predictive models. A Data scientist might need machine learning algorithms to build recommendation systems or automated scoring systems.
Depending on your project, they might need to use technologies like Artificial Intelligence (AI) to build decision-making or classification systems. They might need to lead data analysts or a data science team.
The roles and responsibilities to consider when hiring a data scientist
Whether you have a startup or you are an IT leader in an enterprise, you have specific objectives from a data science project. You need a data scientist to take up specific responsibilities. These are as follows:
- Working with business stakeholders to understand the business requirements;
- Collaborating with MIS reporting executives, business analysts, and statisticians to determine the technical requirements of a data science project;
- Deciding the outline and approach of a data science software development project;
- Establishing a metrics and measurement process to judge the success of the project;
- Leading the data analysis and data visualization efforts;
- Selecting and using appropriate machine learning models for an effective data science system;
- Improving the quality of the data pipeline;
- Implementing effective data analytics tools and developing programs for this when applicable;
- Leading optimization efforts of data science programs;
- Communicating with all relevant stakeholders;
- Collaborating with your larger team including infrastructure architects, business analysts, project managers, testers, and DevOps engineers;
- Leading a data science team;
- Managing stakeholder expectations and reporting project status.
The skills and competencies that you should look for when hiring data scientists
Let’s talk more about the hiring process now. You might have created job postings on LinkedIn and other sites/apps. Thanks to the help from recruiters and hiring managers, you might have received resumes of data science candidates. It’s now time to think about the appropriate interview questions so that you can find the right data science talent.
This job requires a unique combination of skills. Drew Conway’s famous Venn diagram of these skills is a nice visual way of understanding them.
What skillsets should you expect from an experienced data scientist? Apart from a bachelor’s degree in computer science, information technology, or related fields, you should look for the following:
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1. Technical skills that you need in a data scientist or data engineer
You need a data scientist with the following primary technical skills:
- In-depth knowledge of machine learning algorithms and techniques, e.g., Naïve Bayes, Decision Forests, etc.;
- Solid experience in one or more of the following data science-oriented programming languages like Python, R, Julia, etc.;
- Deep knowledge of deep learning and neural networks;
- Good familiarity with popular branches of Artificial Intelligence like Natural Language Processing (NLP), computer vision, etc.;
- Sound knowledge of data science toolkits like MatLab, NumPy, etc.;
- In-depth knowledge of SQL or other query languages like Hive, Pig, etc.;
- Sound experience with popular relational database management systems (RDBMSs) like Oracle, MySQL, PostgreSQL, etc.;
- Excellent knowledge of NoSQL databases like MongoDB, HBase, Cassandra, etc.;
- Sufficient experience in open-source software frameworks like Hadoop;
- Solid knowledge in advanced analytics tools like SAS;
- Experience in Apache Spark, the popular open-source distributed processing systems for big data;
- Familiarity with popular data visualization tools like Tableau, D3.js, GGplot, etc.;
- Experience in predictive modeling techniques like regression analysis;
- Sound knowledge of statistics including proficiency in distributions, statistical testing, etc.;
- Excellent knowledge of Microsoft Excel.
2. General programming skills needed in a data science project
You need the following general programming skills when you hire data scientists and data engineers:
- Common programming and scripting skills that a programmer needs;
- Excellent knowledge of popular cloud computing platforms like Amazon Web Services (AWS), Google Cloud Platform, or Microsoft Azure;
- Experience in developing RESTful APIs;
- Sound knowledge of mitigating application security vulnerabilities;
- In-depth knowledge of developing scalable applications.
3. Software engineering skills
Data science projects require a good deal of software engineering skills too. Look for the following when you hire data scientists:
- Good knowledge of software development lifecycle and development methodologies;
- Deep knowledge of popular software architecture patterns;
- Sound knowledge of various kinds of testing like functional testing, A/B testing, performance testing, security testing, etc.;
- Good experience with DevOps tools, processes, and practices;
- Sufficient familiarity with DevSecOps processes and practices;
- Code review expertise;
- Sound knowledge of software defect prevention methods and practices;
- Excellent knowledge of best practices used in software development projects.
4. Competencies that you should focus on when hiring data scientists
Real-world data science projects involve plenty of complexities. They attract plenty of visibility, and they are high-stakes projects. Executing such projects requires much more than technical skills.
You need the following competencies when you hire data scientists:
- Passion for excellence: A data science project must deliver tangible results, i.e., business stakeholders should be able to gather actionable insights. High-quality data science systems must serve their end-users well. This requires a passion for excellence on the part of the data science team. A data scientist that leads such a team must demonstrate this passion.
- Commitment: Data science projects typically have stringent deadlines. They often have a complex scope, furthermore, they must meet quality objectives. A data science team will face several challenges. The team must navigate them successfully and meet the project objectives. A data scientist needs to demonstrate the commitment to the project and organizational objectives.
- Collaboration: A data scientist will work with the business stakeholders, MIS reporting executive, statisticians, software architects, programmers, testers, DevOps engineers, and infrastructure architects. The role requires regular interaction with a diverse set of stakeholders, managing their expectations, and delivering results. Data scientist needs to collaborate effectively.
- Communication skills: Data science projects tend to have plenty of complexities. Such projects are more complex than run-of-the-mill software development projects, and complex projects involve more interactions. Data scientists need to communicate effectively with a wide range of stakeholders. This will help everyone to be on the same page.
- Leadership: The complexities in a data science project often create uncertainties. In such circumstances, clarity is the key to success. Successful leaders cut through the noise and give clarity to their team. Data scientists need to demonstrate leadership.
Some Tips for Hiring Competent Data Scientists
You’ll be competing against many great companies for the best candidates with all of these qualities. Here are 7 tips to help you identify the right data scientists, and successfully bring them to your organization.
1. Design a great recruitment system
“System “is the key word here. Companies are finding that, on average, it can take hundreds of applications to find a suitable candidate for a data science position. It’s going to take a lot more thought than just putting up a job listing and doing a few interviews to get it right.
You’ll need a system that can:
- Attract the right candidates to apply
- Weed out the wrong candidates efficiently
- Identify the most talented applicants
- Convince your top choices to join your team
An effective recruitment system can be thought of as like a funnel. Hundreds of applicants enter the top of the funnel, and each step of the process eliminates the wrong candidates while keeping as many good ones as possible.
A good place to start is with technical knowledge. There are many more skills required to be a great data scientist, but mathematical and statistical prowess is non-negotiable.
This could take the form of a do-at-home test for your applicants. The test must accurately test the skills your hire will need as part of your team.
The next step of your recruitment funnel is where you’ll differentiate between the good and the great applicants. Technical aptitude is needed, but most importantly, they need to be able to make data work for your business. You’ll need some creative methods to reveal those skills.
This doesn’t need to be a boring traditional interview. It could take the form of a day of problem-solving with your team. Invite everyone that passed the take-home test.
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After a full day of problem-solving and interacting, you should have a much better idea of who to pick. Data science is extremely collaborative, so your whole team should be involved in the decision-making process.
Lastly, you have to offer the job. This step has to be done well when recruiting data scientists. Remember, you are competing for the best. You should take care with this step to make your offer as appealing as possible, and doesn’t come across as arrogant.
2. Cast a Wide Recruitment Net
Once you’ve got your recruitment funnel thought out, you need to start getting people into it. Good Candidates from obvious places, like good tech schools, will be inundated with offers for interviews. These are great sources of talent, but not the only ones.
Data science is a small world. One good way to meet potential hires you wouldn’t otherwise find is through networking. Linking up with individuals from other companies active in this field will be invaluable. Anyone in your network will likely introduce you in a very positive way.
If you’re having problems finding people will all these skills, don’t worry. If a candidate doesn’t have all of the technical skills required, they still might be a good fit. If someone has a talent for communication and analytical thinking skills, teaching them specific technical skills like R or Python won’t be a problem.
3. Use objective methods to avoid bias
Unconscious bias is a problem every recruiter has to deal with. Strangely, even cutting-edge recruitment algorithms designed to avoid this seem to share some of these prejudices. With the competitiveness of data science, it’s just exaggerated. If left unchecked, this can damage your chances of successful recruitment.
Some common biases include the conformity bias, beauty bias, as the halo effect. To deal with them, you’ll need objective methods of evaluating candidates in your process. They can be tests you come up with ahead of time that measure skills you are looking for, where the results won’t be affected by your emotions.
4. Don’t Shoot Yourself in the Foot
There are many mistakes in traditional hiring process that are repeated by a recruiter after recruiter. These can produce false negatives (accidentally turning down a good candidate) and false positives (offering a job to the wrong candidate). Both are bad for your business.
These could be things like:
- Asking interviewees to solve “toy” problems that don’t actually demonstrate real-world ability
- Strange interview questions that have no purpose (we’ve all heard these, e.g. “If you were a fruit, which one would you be, and why?”)
- Coming across as arrogant, bias, or rude
- Demanding free work
- Getting too personal with questions
You might cringe at some of these, or think they are obvious, but these types of things happen in interviews every day. The job of data science recruiters is already difficult – don’t scare away awesome applicants by doing something silly.
5. Get the right kind of data scientist
What many companies don’t realize, is there are two distinct kinds of data scientists.
- Data scientists that deliver to humans
- Data scientists that deliver to machines
The two are very different jobs and require different skills.
The first kind will analyze a business’s data, and come up with ideas and data-driven insights to present to business decision-makers. The results must be presented in a way that a non-techy can understand. This kind of data team will look deep into complex data sets to derive meaningful insights, and present them as stories, graphs, and charts.
The second type of data professional might analyze the same dataset but has very different goals. These will be machine-readable reports that can be fed into the companies’ other business systems to produce automated responses. Things like:
- Product recommendations
- Advertisement placing or targeting
- Buying or selling stocks
This type of data scientist needs razor-sharp statistical and programming skills, in order to build models that can make predictions and decisions in real-time.
6. Make Your Interview Process Sell
Remember, best data scientists are in high demand. Data science is so competitive with in-demand jobs that you have to find the right candidate AND convince them your firm is the best to join. Your whole recruitment process needs to double as an advertisement for how great your company is.
The tests, interviews, and other parts of your process should mirror how candidates would actually work at your firm. (If that’s not a good advertisement, then maybe your firm needs a culture shift!)
7. Make Sure You Actually Need to Hire Data Scientists for Full-time
It’s difficult to hire data scientists – that’s why I’m writing this article. So, you need to ask yourself: Do you really need one full-time or even a freelance data scientists would suffice?
What you are really looking for is a system that takes in your business data, and returns to you valuable, actionable insights. Depending on your type of business, there’s a good chance you might not need an in-house data scientist.
The data science skills shortage has lead to some great companies offering analytics services and data scientists for hire. They can build the systems you are looking for and help you maintain them, without you recruiting data scientists to your team at all. It could be worth checking out.
These days, a qualified data scientists team can mean the difference between a company thriving, and a company fading into insignificance. Making the most of your data isn’t a choice anymore, it’s a necessity. Follow the above-mentioned tips to make sure the process of finding top data scientists isn’t too painful!
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Frequently Asked Questions on How to Hire Data Scientists
It is the science of extracting usable data patterns or other information from data pools. This data can be used for anything from improving product recommendations to fraud detection.
You can find data scientists online through specialist companies. Be sure to vet them to ensure they have the right skills and experience.
Data professionals use computer software programs to extrapolate patterns or data insights from data pools that can be used for purposes such as improving customer satisfaction etc.