Happiest Minds helps customers gain valuable insights from the massive amounts of enterprise data. The firm uses Automated Machine learning & MLOps to boost data scientist’s productivity and embrace AI deployment at scale.
The company internally uses data science for building AI chatbots for IT and HR to reduce wait time, resolve issues, mine data etc.
Data science team
At Happiest Minds, the data science team has a simple agile structure comprising architects and data scientists. From a functional perspective, each data scientist has expertise in more than one area, such as NLP, Vision Analytics, AI at Scale, Statistical, Deep Learning and AI modelling. At present, the data science team includes a total of 35 Data Scientists and is looking at doubling the headcount within a year.
Agrawal stated, “We started building our team 3-4 years back with Data Scientists who had a strong statistical background and from top-notch engineering college. Currently, our focus is more on a PhD, Master’s, or bachelor’s degree in Computer Science or STEM (Science, Technology, Engineering, and Mathematics) or MBA with strong fundamentals and problem-solving skills.”
He also mentioned skill-sets the company looks for while hiring data scientists:
- The ability to discover and solve customer challenges using advanced analytics techniques.
- Hands-on with programming and expertise with a few leading tools and cloud platforms.
- Agility to quickly fathom and develop solutions for high-performance and scalable AI. Envisage and implement effective solutions for short-term and long-term needs.
- Ability to comprehend business requirements and map that to technical data requirements.
- Ability to collaborate with, educate and enable subject matter experts.
- Openness and proactiveness to learn new developments in the AI space.
Data scientist @ Happiest Minds
Agrawal said, “We have carved out the roles and responsibilities for data scientists as part of our Career Framework and Role definition charter at Happiest Minds. We have always been focussed on hiring the best talent, upskilling, and retaining the talent.”
The company provides a complete platform for data scientists to grow, share and adapt. Agrawal added, “The most important thing is that the Data Scientists have the option to shape their destiny to be a Generalist or specialist in a particular area like text analytics, vision analytics, AI at Scale, an expert in a particular vertical or become a full-stack data scientist (Data engineering and Data Science).”
According to Agrawal, the interview process for hiring data scientists depends on the experience level. However, at a high level the company checks aptitude, understanding and articulation of the past work proficiency with programming and experience with the advanced analytical tools and technologies.
Coding skills, especially in the Python language, are a must. Based on the experience, the coding questions are asked to the candidates.
The technical round revolves around questions primarily in Statistics, machine learning, and deep learning.
Agrawal added, “Scenario-based questions and their experience to understand the level of complex problems they have solved. Most importantly to check their critical thinking. At a Senior level, we check for the ability to grasp the big picture and articulate the way forward. Sometimes we do have a coding test initially to filter out candidates.”
The questions mainly depend on the nature of the role. The questions are around:
- Programming (Data Structures, Pandas Library, Numpy operations, EDA etc.)
- Use case and scenario-based (understanding the problem, pipeline, choice of tools and tech, problem-solving, thought process etc.)
- Architecture and Implementation – Statistics/ML/DL/AI related questions
- Past experience and projects (Contribution, Understanding and learnings)
- Attitude and Aptitude (Team Play, Leadership, Learning and Sharing etc.)
“Understanding the problem and situation coupled with your analytical skills, aided with your grip on the tools and technologies is the key to your success. We measure growth based on the impact and not by any other parameter. A focused approach with continuous learning should be an essential ingredient of your journey,” said Agrawal.