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How to judge a good Data scientist with just 5 questions?

Are you still asking questions about Math and Statistics to candidates in a data scientist interview?  Well, if yes! You are asking them a thing that even an average graduate could answer. Remember that a good data scientist may test models in ways that math and statistics can't. They understand how to move beyond the technical constraints and conceptualize their frameworks, making use of a language and approach that any stakeholder can grasp.

Their exceptional contribution to any organization is a major reason why the employment of data scientists is expected to swell up to 35% from 2022 to 2032. Now, this is much faster than the average for all occupations! 

Also, on average, about 17,700 openings for data scientists are created each year. These opportunities are anticipated to emerge as a result of having to replace workers who migrate to other occupations or leave or retire. 

So, there is a dire need to hire good data scientists for the success of a business. Let us go through these five important questions to ask a data scientist and assess the qualities of a good data scientist

Here’s what we’ll cover in this article:

  • Common Challenges in Evaluating Data Scientists
  • 5 Smart Questions to Judge a Data Scientist
  • What are the Qualities of a Good Data Scientist?
  • Estimating the Overall Fit
  • FAQs about a Good Data Scientist

Common Challenges in Evaluating Data Scientists 

Employers face significant challenges when assessing data scientists due to the field's interdisciplinary character. Exploring the candidates to the fullest demands a unique hiring procedure. One needs to ask specific questions aimed at gauging the qualities of a good data scientist. This is the way you will get to know who excels in the broader landscape of data science rather than being just a technical expert. 

But before we discuss the top five questions to judge a good data scientist, let us delve into some common challenges in evaluating data scientists.

Diverse Skill Set 

Data science necessitates a combination of technological skills, statistical knowledge, and commercial insight. Evaluating candidates across various characteristics can be complicated.

Rapid Technological Evolution 

The ever-changing characteristics of data science techniques and technology need ongoing training. It is difficult to find applicants who are up to date on the current developments.

Practical Application vs. Academic Knowledge 

It can sometimes be tricky to distinguish between people who can use theoretical knowledge in practical situations and those who only understand academic concepts.

Effective Communication 

The capacity to communicate difficult facts to a variety of audiences is important. Evaluating a candidate's communication abilities, particularly in closing the divide between technical and non-technical customers, can be difficult.

Evaluating Problem-Solving Approaches

Identifying how candidates deal with and resolve complicated problems, particularly when confronted with unexpected challenges, demands an in-person test. 

5 Smart Questions to Judge a Data Scientist 

It is extremely presumptuous to evaluate a good data scientist with a proper judgment that can be useful to the organization. People with strong fundamentals but lack practical skills and great talkers may not do the real work. Even those with shiny credentials struggle to explain their tasks and metrics after a month.

5 smart questions for a good data scientist 

Here are those 5 smart questions that are good for understanding the real caliber of a candidate. 

1: How Do You Determine an Appropriate Predictive Accuracy for Your Model? 

Best Probable Answer:

  • The potential employer recognizes the significance of predicting accuracy but emphasizes that the greatest probable accuracy is not always desirable. 
  • They talk about the business, the background, user scenarios, and the practical effects of the model's predictions. 
  • The contender demonstrates an awareness of the tradeoffs between accuracy and real-world application.

Worst Response:

  • The candidate argues that reaching the highest level of accuracy is the only goal.
  • They skip over the business environment or practical uses.
  • The explanation lacked a conceptual comprehension of the necessary balance between correctness and practical viability. 

Best Judgment by an Interviewer:

Someone who knows the value of different viewpoints when it comes to predicting validity is more likely to base their work on actual events. Being able to balance accuracy and applicability reveals an improved degree of expertise in data science. 

2: How to Handle Models Going Stale as a Consultant With a Temporary Client?

Best Probable Answer:

  • The individual addresses how to deal with the short-term status of consulting assignments.
  • They provide strategies for making sure that models stay relevant after customer involvement.
  • The potential employee understands that consultancy work is only short-term and intends to maintain the model on a regular basis.

Worst Response:

  • The individual does not recognize the short-term status of consulting assignments.
  • They have no plan or consideration to offer for model management after the customer association has ended.
  • The solution fails to exhibit anticipation and indicates an absence of a grasp of the difficulties of model preservation.

Best Judgment by an Interviewer:

Someone who thinks beyond immediate interaction with clients and addresses long-term model sustainability reveals a more strategic and perceptive mindset.

3: How is the Role of Storytelling in Enhancing Machine Learning Work?

Best Probable Answer:

  • The prospective employee knows the value of storytelling when explaining the positive aspects of data and analytics.
  • They can explain how narrative may make machine learning initiatives easier to comprehend and appeal to clients or partners.
  • The person in question stresses the practical importance of strong interpersonal skills for the accomplishment of machine learning campaigns.

Worst Response:

  • The individual ignores the importance of storytelling in machine learning initiatives.
  • They fall short of offering instances or highlighting how storytelling might increase project effect.
  • The answer indicates a lack of respect for the non-technical elements of communication.

Best Judgment by an Interviewer:

A candidate who appreciates the importance of good communication and narrative is more qualified to reduce the disparity between technical and non-technical clients. 

4: Why is Having a Favorite Algorithm Discouraged? 

Best Probable Answer:

  • The potential candidate emphasizes the need to be versatile and creative when selecting algorithms according to the unique situation.
  • They talk about the drawbacks of particular algorithms and the significance of using the correct one for each specific work.
  • The candidate demonstrates a practical awareness of algorithmic variability.

Worst Response:

  • The candidate tries to follow the question but vaguely and sticks to having a favorite algorithm, irrespective of the exact situation.
  • They underestimate the value of algorithmic variability and agility.
  • The response demonstrates a restricted perspective and an absence of adaptation.

Best Judgment by an Interviewer:

Someone who believes in discouraging one favorite algorithm and respects the broad range of algorithms and their uses can approach problem-solving with flexibility and practicality.

5: How to Persuade Your Organization to Integrate Machine Learning Model in a Product Gradually?

Best Probable Answer: 

  • The prospective employee explains the difficulties of manual collection, focusing on the capacity and future advantages of incorporating machine learning.
  • They offer a strategy for slowly integrating machine learning into the product.
  • They emphasize the possibility of improved results over time.
  • The applicant looks at not only technical concerns but also explains the strategic and business advantages.

Worst Response:

  • The individual advocates replacing the manual procedure immediately, regardless of its current success.
  • They do not make a compelling case for the potential long-term advantages and manageability of machine learning incorporation.
  • The response misses an analytical perspective and a knowledge of organizational functioning.

Best Judgment by an Interviewer: 

Someone who can systematically convey the positive effects of machine learning integration from both short-term and long-term viewpoints has a deeper awareness of business requirements.

What are the Qualities of a Good Data Scientist

Data scientists often wonder how to be a good data scientist. The thing is that one must explore the qualities of a good data scientist and how an aspiring professional can possibly develop them. Apart from technical proficiency, essential skills are 

  • Proficient problem-solving abilities 
  • Good communication to bridge the gap between data and stakeholders 
  • Agility to manage the evolving data world
  • A thorough awareness of the business context

A skilled data scientist uses technical skills to 

  • Provide actionable insights
  • Solve complicated problems
  • Present findings clearly
  • Adapt to changing challenges
  • Align their work with organizational goals. 

This comprehensive skill set enables them to make substantial contributions to the constantly changing sector of data science. 

Estimating Overall Fit 

In conclusion, it is very important to make informed decisions when choosing the best candidates in Data Science. These questions are critical means of assessing a data scientist's practical skills and perspective. Employers should prioritize traits that go beyond technical expertise, such as someone's capacity to manage real-world difficulties and interact well. To succeed in this dynamic sector, aspiring data scientists should acquire a comprehensive skill set. 

However, you cannot know from just five questions. Because data science is an interdisciplinary field, it is hard to grade a data scientist using only five questions. There will be other technical and behavioral questions that will help in the evaluation process. Interview Kickstart is the ultimate place that guides candidates well to become suitable for data science positions.

FAQs About Good Data Scientist

Q1. How is data science used in real life?

Data science has applications in a variety of industries, like healthcare (for personalized medicine and disease prediction), finance (for identifying fraud and risk evaluation), retail (for recommending products and market analysis), transportation (for forecasting repairs and route optimization), and others.

Q2. Who is suitable for data scientists?

Data science is a diverse discipline that demands both technical as well as interpersonal skills. If you have a solid background in statistics, math, and coding and like working with data to fix issues and make predictions, data science could be a good career choice.

Q3. What is the role of a good data scientist?

A good data scientist is someone who can collect vast volumes of data utilizing analytical, statistical, and programming skills. They should use data to create solutions that are personalized to the organization's specific demands.

Q4. How does Interview Kickstart prepare data scientists?

Interview Kickstart strives to prepare aspiring candidates through thorough interview preparations under the guidance of industry ecosystem experts, i.e., FAANG+ Data and Research Scientists. Candidates get to practice via the best of mock interviews, one-on-one sessions, and explicit online lessons. Data Science aspirants and professionals can now nail data science interviews efficiently under the best guidance in a span of just 15 weeks.

Last updated on: 
February 12, 2024

Vartika Rai

Product Manager at Interview Kickstart | Ex-Microsoft | IIIT Hyderabad | ML/Data Science Enthusiast. Working with industry experts to help working professionals successfully prepare and ace interviews at FAANG+ and top tech companies

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