From working in data science at New York Life Insurance to her cybersecurity machine learning projects with Credit Suisse, right down to receiving the Data-Inspired Future Scholarship from Liaison Technologies, engineering management undergraduate student Namankita Rana ’19 is impressing on all fronts.
"I actually got started on the engineering management path when I took a graduate-level class at the School of Systems and Enterprises on data mining with Dr. Carlo Lipizzi," said Rana, who came to Stevens Institute of Technology as an undecided engineer major. "That's what first piqued my interest in the field. I realized that working in data science and machine learning could be my 'thing,' my way of working at that unique intersection of technology and business."
She describes Lipizzi's passion for the field as "nothing short of infectious" and was immediately hooked after learning about his impactful work in the field. Rana, who expects to graduate in May 2019 with a Master of Science in financial analytics and a Bachelor of Engineering in engineering management via Stevens' 4+1 Bachelor’s/Master's Program, translated her quickly-budding passion into two successful internships.
Rana worked with New York Life Insurance in 2017 as a data science intern where she successfully utilized more than 8 million data points to forecast mutual fund redemption amounts within 10 percent of their actual amounts, as well as worked on a production-level data visualization project to predict the primary mortality rates of life insurance clients. In 2018, she spent three months working as a cybersecurity data scientist and machine learning engineer with Credit Suisse. Her projects with the Swiss multinational investment bank included building a model capable of flagging anomalous and potentially malicious behavior from global service accounts, and writing scripts to detect unusual trader activity.
Rana says both roles helped her gain new perspectives on the role of machine learning in a corporate setting, saying its future utilization will empower stakeholders to make smarter, faster decisions while using their data to back them up.
"I think machine learning will be used to derive insights and make predictions that were previously impossible to accomplish by human analysts alone. That’s an incredibly exciting concept, all on its own," she said. "There’s obviously plenty of corporate benefit to be had by implementing machine learning solutions, and machine learning is at the forefront of innovations in fields like healthcare, tech and fashion. However, I think the biggest promise that machine learning will bring is the ability to effect real societal change. If, for example, machine learning solutions are used to help build safer communities, allocate resources in times of disaster, or reduce socio-economic inequality, and they certainly can be, then I would say it’s a job well done."
Between her internships at New York Life Insurance and Credit Suisse, Rana applied and was selected for the Data-Inspired Future Scholarship from Liaison Technologies, which contributes $5,000 to Rana's education, thanks to her winning video entry on how machines can use data to determine trends and then make predictions. She says being chosen for the scholarship was just an added reminder that she had chosen the right career path.
"I applied for the Liaison [Technologies] scholarship before starting my work assignment with Credit Suisse, and found out I'd won it towards the end of my tenure with them. However, applying for the scholarship forimed for me that I really wanted to do work with machine learning, and that I needn't settle for anything I didn't want to do."
Rana is currently seeking to achieve a full-time role in the fields of data science, machine learning and analytics. Her philosophy on being a successful in her future career is simple – be able to translate business needs into technical requirements and, ultimately, a robust final product. A skillset, she adds, learned at the School of Systems and Enterprises engineering management undergraduate program.
"The undergraduate engineering management program does an excellent job of building just that skillset," she said. "Being able to work at that intersection of business and technology is a unique ability that pays off in dividends, but also sets up candidates who are uniquely qualified to work in data science and machine learning if they so choose. From classes like software and informatics, to operations research and data mining, engineering management students get a wide breadth of interesting, challenging classes that translate into excellent workplace skills."
In 2017, 95 percent of engineering management undergraduate students reported at graduation that they were employed, while the remaining 5 percent reported they were returning to their home country. The average starting salary for engineering management undergraduate students varies from year to year, but normally is one of the higher starting salaries for engineers at Stevens. The School of Systems and Enterprises engineering management undergraduate students consistently rank among the top three starting salaries of all engineering disciplines graduating from Stevens, and in 2017 were reported to have a median salary of $65,800.