Founded in 2011 by Ashok Soota, a serial entrepreneur and Indian IT veteran, Happiest Minds boasts a robust data science team comprising over 300 members, including data engineers, intelligence specialists, and data science experts.
Based in the Silicon Valley of India, Bangalore, and extending its reach across the global landscape, including the US, UK, Canada, Australia, and the Middle East, this IT juggernaut seamlessly blends augmented intelligence with the art of understanding human language, deciphering images, analysing videos, and harnessing cutting-edge technologies such as augmented reality and virtual reality.
This dynamic fusion empowers enterprises to craft captivating customer interactions that surpass rivals and set new industry standards.
Happiest Minds distinguishes itself from traditional IT companies by avoiding legacy systems like SAP and ERP, believing that staying entrenched in these technologies limits growth and innovation. “Instead, we have chosen to focus on digital technologies like AI, which is the future of IT,” said Sundar Ramaswamy, SVP, Head of Analytics CoE, in an exclusive interview with AIM.
The team conducts regular market scans to identify the latest technologies and ensures that they are always on the forefront of innovation. This approach allows them to co-create and innovate with clients while building new solutions.
Now Hiring
Happiest Minds is currently on the lookout for a specialist in marketing analytics. The ideal candidate should possess a Master’s or Bachelor’s degree in Computer Science, STEM, or an MBA, demonstrating strong problem-solving skills. They should also have over eight years of experience in the analytics industry, particularly in marketing.
This experience should include a track record of using AI to enhance the customer journey, encompassing areas such as customer acquisition, nurturing, retention, and improving the overall experience.
The technical skills required include proficiency in statistical techniques, ML, text analytics, NLP, and reporting tools. Experience with programming languages such as R, Python, HIVE, SQL, and the ability to handle and summarise large datasets using SQL, Hive-SQL, or Spark are essential.
Additionally, the knowledge of open-source technologies and experience with Azure or AWS stack is desirable.
AI & Analytics Play
This team collaborates closely with domain teams across diverse industry verticals. Their analytics process follows eight key steps. They integrate data from multiple sources, use BI tools for descriptive analytics, perform ad hoc analysis, build data pipelines and auto ML pipelines, retrain models regularly, focus on customer understanding, optimise cloud usage, and ensure data governance.
Their key industry verticals are CPG retail, healthcare (bioinformatics), FSI, media entertainment, and Edtech, with growing interest in manufacturing. The team works with classical analytics, deep learning, computer vision, NLP, and generative AI. This includes advanced applications like language translation and content generation from 2D to 3D images using generative AI.
Recognising the growing importance of generative AI, they have formed a dedicated task force comprising approximately 50 to 60 members, drawn from diverse domains, under the leadership of their CTO with the primary objective to leverage generative AI in addressing industry-specific challenges.
To achieve this, they’ve identified and categorised 100 to 250 distinct use cases across ten different domains, tailored to the specific requirements of each domain. The team is diligently working on creating demos and proof of concepts (POCs) that are domain-specific.
Some team members come from analytics backgrounds, contributing their technical expertise, while others from domain areas contribute to shaping ideas and ensuring results align with the industry’s needs. This undertaking is substantial for the organisation, considering they have around 5,500 employees, with 100-160 dedicated solely to generative AI.
In addition to building demos, the company is also focusing on educating its entire workforce about LLMs and their applications to equip all team members with a basic understanding of generative AI’s capabilities and potential applications.
To bring generative AI into action, the company is working with Microsoft’s suite of products. “We are a Microsoft select partner and are also experimenting with different language models,” he added.
The team initially experimented with Google’s BERT and now employs models like GPT-2. They have a strategic inclination towards refining existing models to suit specific applications, rather than developing entirely new foundational models. For example, they collaborate with a healthcare company to craft adaptive translation models with reinforcement learning.
Interview Process
“Data science is not just about technical skills; it also involves an element of art. Candidates are assessed on their ability to communicate their results effectively and their capacity to approach problems with creativity,” said Ramaswamy.
The interview process for data science candidates at Happiest Minds typically involves three to five levels of interviews. The first level is a screening by the HR team based on the job description. This is followed by a written test to assess the candidate’s proficiency in relevant languages and skills. For example, if the position is for a data engineer, the test might evaluate their ability to work with SQL and other database-related tasks.
Technical interviews are conducted using case studies to evaluate the candidate’s problem-solving ability and approach. The interview process concludes with a leadership interview, especially if the position is a senior one.
In addition to understanding the interview process, candidates often wonder about the common mistakes they should avoid. According to Ramaswamy, there are two main pitfalls that candidates often fall into. First, many candidates focus excessively on specific tools or techniques and become fixated on mastering them.
“While technical proficiency is essential, it’s equally important to explain the problem being solved, the reasons for approaching it a certain way, and considering alternative solutions,” he added.
The second common mistake is becoming too narrowly focused on the solution without understanding the broader context. It’s crucial to see the big picture, why the problem is being solved for the client, and to ask relevant questions about the projects they’ve worked on.
In terms of skills, the company looks for both technical and non-technical abilities. The specific skills depend on the role of the position, such as data engineering, business intelligence, or data science.
However, primary technical skills include proficiency in relevant tools and technologies, certifications, and problem-solving abilities. Non-technical skills are communication and presentation skills, problem-solving skills, and the ability to coach and mentor, as collaboration and teamwork are essential for senior positions.
Work Culture
“As the company’s name suggests, we aim to cultivate a distinctive work culture based on four fundamental pillars,” Ramaswamy commented. Certified as a Great Place to Work, the company prioritises the well-being of their employees, believing that “a content workforce leads to happy customers“. They monitor and maintain employee happiness closely, offering support to those facing personal or professional challenges.
Collaboration is another key element of their culture, as they encourage a unified approach within and across different units and locations. “As a company born in the digital age, Happiest Minds thrives on agility, adapting swiftly to meet the ever-changing needs of customers and the digital industry,” he added.
Transparency is the fourth pillar, as they openly share key performance indicators and objectives with their employees, investors, and stakeholders. This culture of transparency and goal-oriented approach ensures that their efforts are always aligned with clear objectives and tracked diligently.
If you think you fit the role, check out their careers page now.
Read more: Data Science Hiring Process at PayPal
