Typically, every interview ends with one familiar question for the candidate: “Do you have any questions for me?” Majority of the candidates are most likely to respond with either a simple “no” or ask stereotypical questions about the organisation—thereby, letting go of their only chance of making what’s conventionally believed to be a “good impression”. There are many things that are biassed during an interview including the candidate knowing about the role they have applied for. Most inquiries are focused on evaluating the depth of the candidate’s technical knowledge, including their previous data science projects, coding experience, logical reasoning on algorithmic discussions, problem solving and more. Today, with the ever increasing pool of talent, the only way to distinguish individuals is examining how their talents are brought out in the context of the demands of the firms and the economy.

But, it is critical that each candidate be prepared for this allotted time at the end of the interview to ask meaningful questions to their prospective employers. This article describes crucial queries that any candidate could take inspiration from while applying for data science/analytics roles.

Questions for employers

1. How is machine learning currently adding value to the organisation?

Machine Learning serves as a solution to many complex business problems and predicts customers’ behaviours—thereby helping businesses to flourish. The intention for this query is two-fold: 

• To understand the widespread adaptation of AI
• Role of AI in transforming the business

The significance of asking such questions is fully understood when candidates realise that recruiters are looking for knowledge gained through experience. Such queries, while discussing machine learning concepts with prospective employers, will highlight your mindfulness for the applied role. It is also important to learn and unlearn during an interview. As important as it is to appear desirable for the role you’re interviewing for, it is equally—if not more—important to show that you are willing to improve and evolve with the role—which could impress the recruiter.

2.  Can you briefly describe the data maturity levels of the organisation?

The success of any AI project depends on the data quality and data governance practices. Data Maturity essentially talks about the extent to which organisations utilise data and get most out of it. It is important to know whether an organisation does minimal work with the data they produce because then they are most likely in an early stage of their maturity journey. Several organisations aim to use AI to achieve great success quickly and consequently assume that hiring data scientists would solve that problem.

The issue with being in such an organisation is that there is a lack of engagement with the data resources. Leaders and heads of departments rely on data scientists to merely create reports and provide analysis. An organisation with amateur data management practices can negatively impact data scientists’ ability to develop meaningful insights and often end up under utilising their talents.

3. How is success measured for AI projects?

Oftimes, the success of organisations does not solely depend on financial success but also on other factors like:

• Productive leadership with transparency and accountability,
• Focusing more on core principles and less on policy, 
• Having a road map for the future,
• Gaining customer confidence,
• Displaying deep trust in the employees,
• Promoting deserving employees.

Candidates must carefully research all these aspects to understand how their prospective employers/organisations view and define ‘success’.

Simultaneously, it is important to note that organisations find it difficult for data scientists to conduct any kind of research. This is due to the time of production it takes to deploy AI projects. According to Gartner, 80% of these models never reach deployment and fail. Though these Auto-ML models are powerful, in no way does it prove that they can interpret data. It cannot solve all data science problems, which is where the data scientists come in. By asking such a question, the candidate can understand the response from a seasoned data science manager. They must acknowledge the fact that positive outcomes are not solely guaranteed by AI and, in addition, emphasise the role of the data scientist in ensuring better outcomes.

4.  What is the current practice of collecting feedback from business users?

Ideally, business users should remain fully engaged in the project and provide active feedback. Any candidate needs to understand the business maturity of the organisation by understanding customer engagement components such as: 

• Technology
• Readiness and Process
• Activation and Execution
• Marketing Insight and Analytics
• People and Agility

It is crucial to make such enquiries in order to better understand the maturity of the business engagement and current practices/processes in place to obtain the feedback.

5.  What is your management philosophy?

A management philosophy clarifies the expectations for employees and their performance for candidates. It creates a standard for their achievement.

Your supervisor is destined to have a much greater control than you can probably anticipate. From approving leaves to recommending promotions—managers perform a wide variety of tasks. While there is no singular extraordinary management philosophy, it’s vital for candidates to grasp what their prospective manager believes in prior to accepting the role. Are they willing to help you succeed? Or are they more task-oriented and customer-focused? By asking such questions, you are likely to comprehend the vision of your prospective manager—how deeply they believe in AI driving the organisation’s growth and what are they willing to do to expand your role within their organisation?The above mentioned questions are merely a few examples of the kind of questions I believe candidates should ask. Though five or ten minutes in each interview might not seem like a good enough time to get a comprehensive view of the applied role, asking such questions to the people you interact with during the interview process will help you to better assess the role. Having this overview of the role will also help you assess whether you are truly the right fit for the profile. As Steve Jobs once said, “The only way to do great work is to love what you do. If you haven’t found it yet, keep looking. Don’t settle. As with all matters of the heart, you’ll know when you find it.”

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.