How Do You Find Good Data Scientists?

Businesses are continuously refining their recruitment efforts to identify and recruit data scientists who can provide support in examining patterns, utilising analytics, and recognising features and insights that shape product strategy. Data scientists help with the expansive implementation of machine learning and AI techniques to enhance metrics including engagement, maintenance, and returns.

While the demand for data scientists is increasing, most businesses lack a clear hiring strategy for locating and hiring qualified data scientists.

Jeremy Stanley, Chief Data Scientist and EVP of Engineering at Sailthru breaks down his company’s hiring process for data scientists in this post.

The following are the key takeaways:

The foundation of your hiring procedure

If you already have a recruitment process in place, here are three things to consider in order to improve your current hiring strategy.

  1. Increase your accuracy and success rate in locating exceptional employees and convincing them to accept your offer.
  2. Reduce the likelihood of good candidates dropping out of your hiring process too soon.
  3. Ensure that the hiring process does not take up a lot of your time and effort.

Along with these factors, you must lay a solid foundation and develop a set of core principles to ensure that you meet all of your goals.

Here are the fundamental hiring principles you should follow:

Make sure your hiring strategy is an ongoing one.

Creating a recruitment process that can be sustained over time is essential for any successful business. You should aim to make your hiring process an ongoing priority, rather than a seasonal one. Devote the necessary time and resources to constantly seek out, review, and hire the best and most qualified candidates, regardless of the time of year. By creating an ever-evolving and continuously improving recruitment campaign, you can ensure your business will have the right people in the right positions.

Rather than asking standard interview questions, it is important to tailor your questions to the specific role you are looking to fill. Doing so allows you to gain a deeper understanding of the candidate’s qualifications and capabilities and provides you with a clearer picture of how suitable they would be for the position.

Ask questions that are specific to the tasks you expect the candidate to perform once they join the team.

“Understand the issues your team is currently dealing with and the challenges you expect successful candidates to face,” Stanley advises.

To reduce your biases, create an objective hiring process.

Strong programming and quantitative skills are required when hiring a data scientist. So start your interview by putting these skills to the test.

Once you have evaluated candidates on their technical skills and qualifications, you can then move on to assessing their soft skills, including their ability to communicate effectively and their problem-solving capabilities. Finally, you can evaluate how well they would be suited to work in your organisation and as part of your team.

It is important to not rush to a conclusion about a potential candidate solely on the basis of their compatibility with your company. Doing so could lead to a biassed perspective that may result in the premature dismissal of a viable option.

Create a structured hiring process to make a good first impression.

The majority of recruitment processes involve multiple interviews with different people, during which similar questions will be asked. Following the interviews, candidates must then wait for a period of time before receiving feedback on their application, and frequently, their queries may remain unanswered.

Your hiring process will be their first introduction to how your company operates, so you must have a clear structure in place.

Provide prospective employees with a comprehensive insight into your company’s culture and the challenges that they may encounter while working with you and the rest of the team. This will give them a better idea of what to anticipate upon joining your organisation.

Participate in the team in decision-making.

Have a well-defined and tangible framework for evaluating candidates.

Additionally, if you anticipate that the data scientist will need to work with other departments, it is imperative to involve relevant key stakeholders from those teams during the final selection process.

Accelerate your hiring process.

While it is beneficial to take your time in thoroughly evaluating your candidates, you risk losing out on good prospects to your competitors.

In order to ensure a smooth and efficient hiring process, it is important to expedite the movement of potential candidates through the different stages. Investing in suitable tools and infrastructure that is capable of monitoring and analysing the various phases of the recruitment process can assist in identifying areas that require further improvement in order to maintain the momentum.

Interviewing stages

A data scientist interview consists of six stages.

  1. Stage of pre-screening

    It is possible to review each candidate’s resume, prior experiences, and qualifications by accessing them here. Nevertheless, in order to ensure that interviewer bias is avoided, one can opt to forgo this stage and instead send a take-home test to all prospective candidates.
  2. Take-home examination

    This brief test assesses the candidate’s ability to solve problems in their chosen field.
  3. The sales pitch

    This is the stage where you persuade candidates who have passed your take-home test to attend the interview.
  4. Data collection day

    In order to continue the selection process, we will invite potential candidates to a full day interview. During the interview, we will provide them with an open-ended challenge and they will be required to come up with a suitable solution and present it to us. This part of the interview will allow us to assess the candidate’s abilities in a simulated and controlled environment.

    Preparation is the most important factor in making this process go more smoothly
    • As soon as they arrive, have a set of printed instructions ready.
    • Provide each user with a laptop that is pre-loaded with the necessary tools and software programs to carry out their work. This should include HomeBrew, Eclipse, Anaconda (Python distribution), RStudio, R, Emacs, Vim and Java 7. This will ensure that the user will have all of the necessary data and programs readily available to them.
    • Request feedback from candidates and incorporate it into your process to meet the needs of your company.
  5. Final call

    Stanley suggests using these five factors as cornerstones of your hiring decisions.
    • Structure of the problem What assumptions did they make, and what strategy did they employ to limit the scope of the problem?
    • Technical abilities. How readable, dependable, and adaptable is the code presented?
    • Analytical abilities How logical is their reasoning for the approach they presented?
    • Communication abilities. How effectively did they present their work, strategy, approach, and conclusions?
    • The worth of their work. How valuable would your new hire’s work be if it was fine-tuned and implemented?
  6. Communication

    The last step of the process is to communicate directly with applicants. It is important to ensure that Data-day candidates receive feedback, even if the outcome is not positive, so that they can take away something worthwhile from their experience. Following up with candidates who did not receive an offer letter is integral to this process.

    The implementation of a well-structured hiring process can be beneficial in uncovering potential candidates whose qualifications may not be immediately apparent from their resumes or applications. This comprehensive strategy eliminates any guess work from the hiring process and instead provides you with the necessary data to make an informed decision.

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