While it is not common for any professional to contemplate a career transition in their thirties, Prathap Rajaraman’s move to a data-oriented career was much more planned than it was a leap into the unknown. With a degree in actuarial science and half a decade of insurance and finance experience, he had at least direction, he just needed to get there.
Prathap Rajaraman kicked off his professional career with an actuarial science degree from Penn State and through a series of roles across a few sectors began to understand how companies use risk, manage people through multi-faceted efforts, and drive change. However, it was not until he grew into a more lateral data modeling and automation mentality that he began to appreciate how much data science provided improvements to everything he was already doing.
For him, he did not think about changing careers into a more data-oriented career, it was a progression instead. Data science was not going to just provide a better way to code or a better machine learning model; it presented a way to see patterns to find a story, improve decision-making ability, and provide value back into the real world.
A Ground Level View of AI in Financial Advisory
In financial services, there are increasing volumes of AI available, and a certain expectation that we are moving towards an environment without role as we have known them. While Rajaraman acknowledges the coming of age of AI, he does not necessarily view it as healthy for financial services to remove people from the process. Rather, he views AI not as a replacement but as a way to make smarter and faster decisions, with the people closest to the problem. He is particularly interested to see how machine learning can develop underwriting and claims automation by removing some of the monotonous and repetitive work. In Rajaraman’s experience, automation has been useful to reduce errors on the job, shorten turnaround time, and allows professionals to spend their time on the items that have the largest impact. However, it is important to note that the system, and whoever is using it, must still be explainable if it is auditable. Trust in financial advisory relationships is paramount, therefore any algorithms are designed to support explainability and transparency.
Rajaraman does not believe in artificial intelligence as a black box. Just like any other components of a business, there must be accountability for data practices. By integrating ethical principles into the data pipelines, he believes the industry can move forward in an appropriate manner, while developing trust with consumers.
Ethics as the Framework for Data
With data comes responsibility. Rajaraman does not shy away from the ethical side of data science. He feels that being solely technically proficient is outdated; data professionals should also be asking themselves difficult questions about the origins of their data, how it is being used, and what the unintended outcomes might be.
In the fast-paced world of data, where we are often more focused on expediency than reflection, Rajaraman advocates a more reflective process. He ensures the data used in projects comply with regulations and with a sense of right and wrong. He does not consider ethical practice as obstacles, but as frameworks that support keeping businesses in stepping with public expectations.
This view is a part of what distinguishes him in an industry that prizes speed. It is easy to create models that predict behaviors; but it is harder to ensure they do not reinforce bias or erode individual privacy. Rajaraman sees this as just part of his role.
Data Projects that Established Credibility
Credibility is earned, not claimed; and Rajaraman has a portfolio of projects that demonstrates that. There was one project in his career that stands out that captures the tension between speed and accuracy in a high-stakes situation. The aim was to create a risk model that enables underwriters to make faster decisions while maintaining quality. By collaborating with representatives from each area of the company, he was able to build a tool that met compliance, worked efficiently, and all benefits the employee performing the job on the front line.
Another example is his work surrounding data governance. Instead of creating a model and running with it, he endeavored to create a framework to implement their model and case studies, which could be referenced for future projects. This type of long-term vision is invaluable for organizations, yet often gets overlooked.
When I asked him what helped him build that credibility, he pointed to the basic principle of showing your work. Whether he’s creating documentation, developing a presentation, or taking a business leader into a complex model, Rajaraman shows his work. Showing your work breeds trust and helps put people at ease, while also encouraging them to engage with data rather than shy away from it.
The Human Element of Being a Technical Person
When he’s not looking at numbers Rajaraman stays grounded by his family, Mother Nature, and personal development. He enjoys competition running races with his family and finds these activities outdoors to be a great way to re-energize as they also offer him perspective and balance, especially now that he is in a career that requires prolonged isolated focus that often calls for long-hours.
As a mentor, Rajaraman has a very strong commitment. In recognizing how difficult breaking into the category of data science can be, particularly for people at a later stage in life, Rajaraman often recounts his experience in the past. He would tell them to start by getting proficient in statistics and programming, but then realize that is only the beginning. Data scientists become “data scientists” not only by being nuts and bolts strong data practitioners; they also understand how their work influences the users, customers, and stakeholders.
Lifelong Learning as a Driving Force
Prathap Rajaraman does not consider learning to be a completed activity as it is at the conclusion of formal schooling. Learning is ongoing and empowers him to maintain his sharpness and adaptability. His natural curiosity pushes him to explore new ideas in addition to just new tools or frameworks; he reads broadly, seeks out perspective from people in other industries, and actively seeks out coaching and advice.
When he thinks about the things he potentially would have done differently starting out, one of his reflections was that he wished he acquired and incorporated more learning about product customer experience. This really resonated. In other words, it is indispensable to build technical knowledge about the tasks you may be engaged in, but developing empathy and awareness enable impact.
This mindset is also what enables Rajaraman to effectively move across roles and responsibilities. He views change not as disruption, but as opportunity for improvement and refinement. It is a mindset that has served him well and continues to guide him in his work today.Â
Advice for Those Contemplating the Journey
For those contemplating what a career in data science might look like, Rajaraman offered some straightforward insights. Get comfortable with ambiguity. Ask questions. Most importantly connect the dots and consider the people behind the numbers.
It was Raamaran’s point that data is just noise without the context. Its true first value comes when you thoughtfully, ethically, and clearly apply actions, as a business, using data. Irrespective if you are working on a predictive model, new framework, a dashboard, or presenting to executives, understand the foundational purpose of your work.
Building a Thoughtful and Sustainable Career Path
Prathap Rajaraman‘s story is one of intentional progression, reflection, and ethical intent. He did not enter the data science field with a signal value proposition like flashy titles or speed to rewards. Rather, he viewed it as a real way to have a meaningful impact across industries that touch the lives of people every day.
Rajaraman is clearly at his best when he is working on predictive models, interpreting regulatory frameworks, or mentoring the next generation. Regardless of the focus, he adds clarity and purpose to the process. Rajaraman’s journey serves as a reminder that reinvention is possible; and with the right mindset, a technical career can also reflect the deeply human.
