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Checklist For Hiring Senior Executives In Analytics

Checklist For Hiring Senior Executives In Analytics

Debolina Biswas
  • We have curated essential pointers to keep in mind while hiring a senior executives in analytics.

Organisations, irrespective of their size, are waking up to the importance of leveraging data to make business processes seamless. The growing need to make sense of data has increased the demand for data analysts and data scientists. 

Below, we have curated essential pointers to keep in mind while hiring senior executives in analytics. 

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Hiring process

The hiring process and duration varies depending on the organisation. 

Typically for Analytics hiring, the entire process might last anywhere between two to three weeks, depending on the seniority of the position. Usually, it involves two to three rounds of interviews. The first round is a technical assessment round, wherein the candidate’s problem-solving capabilities are evaluated. 

The second round is usually a coding round where the employers share a problem statement or case study with potential candidates and they have to come up with the best possible solutions. “Sometimes companies just want to check candidates’ approach towards problem-solving,” said Khushboo Gogoi, Head of Business Unit (Talent Acquisition) at Analytics India Magazine.

The final round is the HR round. 

Every candidate is a potential employee

Hiring at any level involves a deep understanding of the candidate’s experience. The hiring manager should see to it that all candidates are treated right at every interview round. The HR team should ensure candidates are provided with adequate information about the job role; discuss the prospects of the role, and the interviewer must show interest while conducting the interviews. 

Educational background 

Hiring for any senior position in analytics, for a Data Science role or an Advanced Analytics role, companies check if a potential candidate has a quant background. That is, if they have a postgraduate degree in Statistics or Mathematics or Economics or Operations Research– the very foundation of Analytics. 

Many companies prefer candidates with PhDs from an Ivy League or have a strong background in analytics, good research experience or done a thesis in analytics and data science as part of their course work. 

Experience 

Apart from the skillset, recruiters look for what a candidate brings to the table. A candidate with experience in setting up the Analytics Center of Excellence or building a Data Analytics and Artificial Intelligence team from scratch gets preference.  

“Factors like the kind of people they have hired in the past, clients and geographies they support, all contribute to the selection process,” Khushboo said. 

Spotting frauds 

Often there might be few cases of candidates trying to mislead the hiring team. “Many times, candidates applying for a Data Scientist’s position put in statements like ‘Expertise in Machine Learning, NLP and Deep Learning,’ without really having hands-on experience in these topics,” Khushboo said. 

In such cases, hiring managers should ask the recruiters to assess the candidates by asking them a few basic questions based on the job requirements during the initial screening process.

Being on page with the hiring manager 

“It is imperative to understand the nerve of the Hiring Manager to be able to close a leadership position in any organisation,” Khushboo said. 

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A recruitment manager should have a detailed understanding of the hiring manager’s work, educational background and his/her hiring patterns (educational institutions or qualifications they prefer, or preference in terms of past organisations they have hired from). This not only reduces the time taken in hiring but also makes the process more efficient. 

Cultural fit 

Hiring for a senior executive role means the selected candidate will eventually become the face of the organisation. Therefore, it’s essential to ensure the candidate is a good culturally fit for the organisation. Additionally, the recruitment manager must make sure the role aligns with the candidate’s career aspirations. 

In the case of startups, it’s important to figure out if the candidate would be comfortable working in a fast-paced environment. “Sometimes, there are cultural mismatches. This happens because large organisations are more process-oriented, while startups have an open-door policy,” Khushboo added. 

Startups look for people who are flexible and readily adapt to evolving technology. Candidates who have worked in large organisations often find it challenging to adjust to the startup environment. 

Most of the Senior Data Science positions with startups these days look for people with hands-on experience in coding. Candidates coming from larger organisations are used to delegating coding workload to their team and focus more on people management. 

Personal aspirations 

Finally, while speaking to the prospective candidates, it is essential to understand their career aspirations — their next big move; the kind of organisations they have been a part of in the past; where do they want to be in the next five years of their career — these are important questions that recruiting managers must ask while interviewing for a senior executive role in analytics.

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