Q1. Starting your career as a data scientist to now serving as the data science leader and building data science teams, what are the key lessons you would like to share with professionals in their early careers?

I graduated as an engineer and started my journey around 2010/2011 when AI, data science, machine learning were driving impact across some particular areas/industries. AI was not mainstream as it is now. HBR published “Data Scientist: The Sexiest Job of the 21st Century” in 2012, so it was before that.

Looking back on my own journey, firstly I will acknowledge that technology has changed, become more advanced and accelerating at even greater speed. So here are a few things I will share with people in early careers in this field, these are also my guiding principles

1. Embrace the messy data- Data science does not mean clean code and elegant algorithms. It is about working with messy data, navigating ambiguity, and finding creative solutions to often vague problems. Don’t be afraid to get your hands dirty – that’s where the real learning happens.

2. Drive business Impact – Technical skills are crucial, but true success is underpinned by understanding the business problem and driving impact. Focus on translating data into actionable insights, and driving real-world impact. Remember, it’s not about the model, it’s about the money it saves or the value it creates.

3. Communication is the key – We should be able to communicate complex ideas clearly and concisely to both technical and non-technical audiences if we want implementation of complex models

4. Learning: Cannot stress this enough specially in today’s ever changing world, our field is in state of constant evolution. stay curious, and actively seek out new challenges and technologies. There’s always a new algorithm to master, a fresh perspective to understand. Be open and adapt the concept of experimentation, fail fast & learn faster, and iterate your way to success.

Most important – Enjoy the journey, data science is a challenging and rewarding field and you can drive difference. Future is data-driven, and you’re in the driver’s seat.

Q2 Generative AI has been disruptive, and the technology is changing so rapidly that it has affected the way businesses are run. How are businesses are using it as an opportunity?

Generative AI (GenAI) isn’t science fiction anymore. It’s quietly entered  our lives, through the tech powering our smartphones to autonomous-driving features on cars, tools that retailers use to engage consumers. When ChatGPT was launched, it got 57 million monthly active users in its first month of availability, nowadays with video, audio and text generating AI tools gen AI has become even more mainstream.

There are immense number of use cases and we are still learning but there have been some key areas where industries have been using GenAI to drive business value in these recent years. As per a recent Mckinsey study, 75 percent of the value that generative AI use cases pan across Customer operations, marketing, software engineering, and R&D

Generative AI has unlocked new avenues for enhancing customer experience with chatbots, other AI tools which customer services reps can use which drive significant increase in issue resolution in much less time than traditional methods

In marketing, businesses are leveraging generative AI algorithms to create high-quality content at scale, from articles and videos to music and artwork. By automating the content creation process, businesses can personalize their offerings to drive customer engagement. For example,  Spotify uses AI to create personalized listening experience. Their AI models analyze factors like listening habits throughout the day, mood, and even external influences like weather patterns.

In the realm of software, generative AI serves as a coding assistant, accelerating development cycles and empowering engineers to focus on innovation. By automating routine tasks, AI tools have revolutionized the way software is built. Github copilot is an example of coding assistant

Additionally gen AI is accelerating product innovation and reducing time-to-market. Whether in pharmaceuticals, fashion, or consumer products, AI-powered tools facilitate the exploration of new solutions. For example Hell AI developed by Hungarian beverage producer. The company leveraged a generative AI platform to create a new flavor that it predicts will appeal to a wide range of consumers.

Q3 With the surge in online shopping and changing consumer preferences, understanding customer behavior has become crucial for retailers. How do you leverage AI/ML technologies to uncover actionable insights into customer journeys and segmentations, developing effective customer strategies and drive growth?

In the booming world of retail and ever-evolving customer preferences, retailers need a deeper understanding of their customer base. With the advent of AI/ML there has been a tremendous improvement in terms of use of data to unlock actionable insights into customer journeys and segmentation, empowering them to develop customer strategies and drive growth:

1. Data: First things first, businesses gather data from every corner– website clicks, purchase history, abandoned carts, transactions, supply chain you name it. It’s like collecting puzzle pieces, but unstructured (data professionals will understand!). We can then leverage technology to clean and organize this data, to get a picture of customer behavior.

2. The Power of Patterns: There are numerous AI/ML algorithms which can sift through the data to identify hidden patterns. They can reveal customer segments like the high-spenders, the discount seekers, health focus group etc.

3. Predicting: Here’s where things get exciting. We can build and train ML models on historical data to predict future customer behavior. What products are they most likely to buy next? What else can be bought with this? This empowers retailers to personalize the shopping experience, recommend relevant products, and ultimately drive sales.

4. Sentiment Analysis: AI doesn’t just analyze actions, sentiment analysis helps to analyse customer reviews and social media posts to understand what customers are saying and identify major feedback. This feedback helps retailers identify pain points and areas for improvement, to drive better customer experience

5. Actionable Insights that drive business value: The ultimate goal is not just fancy model or a prediction, but real-world impact. Retailers need to translate the insights into actionable strategies. This could be targeted marketing campaigns, personalized product recommendations, or revamping the payment system experience for greater ease in case of an online retailer

Companies like Netflix, amazon, spotify and many more are great examples for all of the above. I will give another which I recently read about, a company called Peloton, they are into interactive fitness equipment. They utilize AI to provide personalized coaching and feedback during workouts. Their AI algorithms analyze workout history to offer real-time adjustments to reach fitness goals

Q4 How Can Businesses Address the Challenges with Generative AI, While Still Leveraging its Potential?

The amazing results and promise of generative AI make it seem like a ready-set-go technology, but that’s not the case. It behooves industries/ businesses to proceed with caution. Technologists are still working on practical and ethical issues with generative AI.

For example ChatGPT, sometimes “hallucinates,” meaning it generates inaccurate information in response to a question. Hence there is a need of built-in mechanism to handle this and point out to the user. There have been instances when the tool was asked to create a short bio and it generated several incorrect facts for the person, such as listing the wrong year of birth There is also a need to create effective filters to catch inappropriate content. Systemic biases still need to be addressed for example resume filtering tools preferring male candidates for tech jobs. These systems are trained on massive amounts of data that might include unwanted biases. Hence there is need to handle these challenges

Data Privacy and Security, protecting customer data and ensuring data privacy and security are paramount. There is a need to implement robust data protection measures, including encryption, access controls, and anonymization techniques, to safeguard sensitive information generated or processed by Generative AI systems.

By adopting a holistic approach that balances risks and rewards, businesses can unlock the transformative benefits of Generative AI

Q5. How do you support the professional growth and development of your team, encouraging creativity and out-of-the-box thinking in their approach to solving complex problems?

During my career I have seen our industry explode from possibilities to the transformative giant it is today. But let me tell you, the most exciting thing isn’t the technology – it’s the people behind it.  I am a people person and I gain energy from people around me. Working with brilliant teams, engaging discussions, brainstorming on why or why won’t some thing work or don’t work, I find it pretty fascinating. So, this is one of key questions I would say.

Regarding professional growth and development, we must understand there is no one size fits all. Every team/individual there is a different recipe, but I would say some key ingredients are common. I believe in encouraging teams to question, not just acceptance but questioning things with a healthy dose of curiosity.  We should challenge assumptions, both our own and each other’s. Whether it’s finding a new way to solve an old problem, solving something entirely new, or even pushing the boundaries of our current project. Creating an environment where we can discover, experiment, learn and iterate is beneficial

As I mentioned earlier continuous learning is one of the core principles for me so I try to enable the same by various methods be it training, self learning or team discussions, participating in workshops, etc. Finally, learning isn’t a one-way street, knowledge sharing is paramount! People look forward to an environment where we benefit from one other’s experiences and knowledge.  To fully utilise the power of teams in our professional settings, we must work to establish an environment where everyone can benefit from the collective genius of our teams.

Author:Himali Bhasin
Delivery Head- Data Science& Analytics
Circle K