With analytics and data science groups that are aligned with various account portfolios, Ugam, a Merkle Company, has a centralised team structure. With a data science team of more than 200 people, the data science hiring process at Ugam is quite extensive to ensure that they have the best talent on-board. We got in touch with Navin Dhananjaya, Chief Solutions Officer, Ugam, a Merkle Company, to understand how the data scientist hiring process looks like at Ugam.
Data Science Skill Sets
To begin with, Dhananjaya shares that from a technical perspective, they look for data scientists who are well-versed with the following tools:
- Data mining tools like R, SAS, Python, etc.
- Big data platforms like Hadoop, Hive, Spark, etc.
- Advanced analytics (conceptual and practical knowledge) which includes clustering, regression models, etc.
- Data visualisation for business insights through Tableau or Qlikview
“Apart from technical skills, we also look for a cultural fit with traits such as curiosity, learning mindset, and an entrepreneurial spirit – which we believe is integral for long-term success, “ he said.
In terms of educational background, engineering in computer science is a preference but not mandatory. Having said that, Dhananjaya shares that while the educational background is important in building foundational knowledge, at Ugam, they give more weightage to skills.
“We believe skills need to be constantly upgraded to keep up with changing times. Hence, we look for candidates with a continuous learning mindset, the right attitude, approach, and determination to keep upgrading and reinventing themselves to stay ahead of the curve,” he said.
Dhananjaya shares a few key pointers that make a data science candidate an ideal fit for the company:
- Customer-centric: Ability to understand customer context, pain-points and think ahead in terms of what the customer would need
- A problem-solver: Structured thinking and a problem-solving mindset to help clients solve the problem at its roots and not just its symptoms
- Consultative: Ability to actively listen and engage, ask the right questions, understand the business problem, and combine it with domain knowledge
- Proficient understanding of basic tools & techniques: Ability to understand and apply the technical know-how in statistics, algorithms, machine learning, deep learning, etc. Should also be well-versed with SQL, Python, R, and other programming languages
- Accountable and trustworthy
- Curious to explore various algorithms, and approaches, and have a continuous learning mindset
- Team player
Data Science Interview Process
The data scientist hiring process at Ugam begins with a recruiter assessment that helps in narrowing down on a candidate. This involves resume screening to understand candidate profile, sieving out fake resumes to arrive at a shortlist. “Our teams speak with potential candidates, explain the role and expectations, understand their expectations, and decide if they make a match for the requisite role,” he said.
The next step is preliminary SQL assessment where shortlisted candidates undertake a platform-based SQL test which comprises building complex queries and answering multiple-choice questions. The test provides an instant score which helps in identifying if the candidate is a technical fit.
The third step is the technical assessment, where they delve deep to understand the specific experiences of the shortlisted candidates. Apart from assessing the technical capabilities of the candidate, the expert panel also assesses their presence of mind, reasoning skills, and invest time in understanding their background, strengths and how well they fit the role.
Finally, a business case study round is conducted, which helps in understanding how well a candidate can apply their conceptual knowledge in practice. Shortlisted candidates are presented with a real-world business problem in the form of a case study. “Here we gauge their thought process, critical thinking and problem-solving skills,” added Dhananjaya.
Dhananjaya shares that the process gives the candidate a close to a real experience of what their role would entail, introduces them to our culture, and empowers them to make a decision for themselves. “This ensures a great success in retaining data scientists, and, therefore, our attrition rate is lower than industry standards,” he said.
Some of the primary outbound channels of recruiting data scientists at Ugam include job boards, social media platforms, campus drives, internship programs, hackathons and other competitions in collaboration with campuses and event forums. Whereas, some of the inbound channels are employee referral programs, inquiries from Ugam’s website and on-premise workshops (prior to COVID-19).
Ugam is currently hiring for senior analyst and lead analyst positions. One can apply to these positions here.
Data Science Interview Questions Asked At Ugam
Dhananjaya explains that the interview process includes questions depending on not only the specific role they are hiring but largely centred around the candidate’s skill, aptitude, attitude, and motivations. Here is an overview of questions that are typically asked at Ugam for hiring data scientists.
- The interview questions are primarily practical and application-based. Questions are based on the business problems solved in their past roles to check for depth of knowledge and understanding. They are also presented with newer, real-world cases, taking candidates out of their comfort zone to test their thinking, approach, and problem-solving.
- In some cases, candidates have even been given a stipulated time on a fuzzy problem and asked to come back with a working code of the solution which is then tested on a hold-out dataset.
- Attitude and motivations are assessed based on how the candidates have made key decisions in their careers and personal life.
Growth Opportunity For Data Scientists At Ugam
Data scientists at Ugam have a good opportunity to work with the business teams of leading Fortune 500 companies in the US and drive impact by solving real-world business problems. In addition, hands-on training and exposure to work on latest technologies in AI, ML, etc. Some of the other growth opportunities are:
- Leadership exposure
- Flexibility to move internally and assume new roles within the organisation and across geographies
- Flexibility to grow individually or as a team manager
- Avenues to nurture soft skills, and more.
Challenges While Recruiting Data Scientists
Dhananjaya is quick to add that candidates tend to oversell themselves on resumes. “Sifting through and reading between the lines due to keywords stuffed on resumes could be time-consuming and challenging. Second, data scientists with a multidisciplinary approach are the need of the hour. It is challenging to find a pool of data scientists who can not only code but also connect the dots with their critical thinking capabilities and business acumen thereby enabling clients to make meaningful decisions,” he said.
To source the right candidate and Ugam, therefore, follows a holistic hiring process that assesses technical, functional and behavioural aspects. “Some of the ways we ensure this are, clear and transparent communication, testing data scientists with real-world examples, and most importantly ensuring a cultural and organisational fit.
For analytics professionals who wish to carve out a career in the analytics industry, Dhananjaya advises to nurture a customer-centric approach and understand the macro picture. ‘Learn, unlearn and relearn’ is another advice that he shares to keep up with the market dynamism. Finally, he quotes co-founder and CEO, Sunil Mirani — good people with good skills are far better and more valuable than an excellent piece of code.