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, and 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 a 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.
The future is data-driven, and you’re in the driver’s seat.
Generative AI has been disruptive, and the technology is
changing so rapidly that it has affected the way businesses are
run. How are businesses using it as an opportunity?
Generative AI (GenAI) isn’t science fiction anymore. It 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 an 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 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 experiences. 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 a 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.
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 a 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
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 for a built-in mechanism to handle this
and point it 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
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 something work or
don’t work, I find it pretty fascinating. So, this is one of the
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.
