From Data Science Intern to Employee – Leveraging What You Learn with Sany Mathew
What led you to choose Pitney Bowes as an employer?
As you know Data Science covers a broad area and so I was not only looking to apply my knowledge but also work in an area that had interesting business models. About six months before my graduation, Pitney Bowes visited the CMU campus. I discussed some of the interesting and unique business challenges PB was having at the time and was excited by the idea that an organization was dedicated to innovation. I was extended an offer and started working in the Innovation group in the Summer of 2015. Upon my degree completion, I was offered a full-time job as a Data Scientist and have been here a little over 2 years now.
Tell me a little about what your experience as an intern at PB?
When I started as an intern at Pitney Bowes, I got a chance to explore what real-world projects in the Big Data space looked like. The learning curve in making a transition from an academic to a business setting was eased by the constant guidance of my peers and mentors in the team.
I learned a lot during my internship – particularly around technology and tools. In the innovation team, we were given the freedom to use any technology we wanted to solve the problem. This helped me experiment on a lot of tools & platforms that helped gain valuable experience, and made life easier for solving a lot of problems. In many ways, it helped me to be faster in my work, by identifying the right tools for the job.
One of the biggest takeaways from my internship was the constant need to present information in as concise a manner as possible. Explaining the otherwise complex advanced analytics techniques in a simple manner and presenting the results to business in regular intervals helped me boost my presentation and communication skills.
How have you grown in your own experiences since you first started as an intern?
As I grew as a Data Scientist with the team, I got opportunities to work across different functions of the company. When I first started, I naturally limited myself to areas I was more comfortable in. As time passed, I found the more interesting projects required me to learn new skills and take some risks. I started venturing outside of my comfort zone and picking up tasks that gave me a lot of hands on experience.
This helped me in two ways – I became more versatile in my skill set, and I learned more about the PB brand and its industry value to the marketplace. It’s one thing to read a glossy about a company or look at its financials and marketing material. It’s quite another to develop an almost intimate relationship with the mechanics of what keeps customers loyal. Much of my work has been on customer retention. That gives you a really deep appreciation for what is valuable and the kinds of projects you want to be involved with to grow your career.
I started searching and reading artifacts of projects that have been previously delivered and tried to align my projects along those lines. I reused existing frameworks, often tweaking them to each project, and learned from the experience of others. That’s a critical skill to any Data Science function. There is simply too much for any one person to know. But connecting with your peers requires not only a common base of Data Science knowledge – but understanding the various data sources and processes that connect products and functional responsibilities like marketing, sales, and support. Identifying and creating these artifacts and helping others use my own expertise helped me present my projects concisely and with self-confidence. It ultimately helped me gain valuable feedback on what I should improve.
For a new intern what would you recommend they focus on when starting a project?
Ask a lot of questions – From personal experience, I feel that when interns start on any project, they’re often caught in a void. Be it understanding the requirements or the scope of the project, there is often a lack of clarity that hampers the speed at which an intern can start on a project. I would recommend that they focus on asking the right questions that helps them gain momentum and place themselves in the right path, instead of going on a tangent.
Focus on the bigger picture – Sometimes, you’ll end up with projects that aren’t exactly what you signed up for. For me, some projects, while not heavily focused on data science (in the traditional sense) helped me build on my data wrangling, data preparation and ETL skills. I also became quite effective at applying different techniques used commonly used across the industry for this purpose. As an intern you should see each new opportunity as a chance to explore new technologies and leverage its capability to identify strengths and weaknesses of “hidden information” as a data source. All these have constantly helped me to edge closer to being a versatile Data Scientist. Somewhere down the line in the future, these side projects that weren’t part of your job descriptions, will help you gain competitive advantage and help you stand out.
What recommendation do you have to getting started as a Data Scientist at PB?
My advice to budding Data Scientists in the organization would be to start exploring the datasets that they have access to. Try to blend these in with some other datasets and try to ask questions that would provide interesting answers. I also read aggressively articles on publishing platforms like Medium that provide recommendations on taking a data-driven approach to problems.
Thinking “data-first” to any product is particularly crucial to gaining insights from your product during its journey. As always there are a lot of resources available on Udemy, Lynda, Coursera and the like. PB’s data portal also provides access to different curated datasets that can a provide a ground to practice your advanced analytics skills and provide value to the company.
Talking to other data scientists and participating in hackathons is also a good practice to stay abreast with common methods, technology used in projects and brushing up your data science skills.
Thank you Sany for providing your perspective on being an intern and sharing your experience with joining PB to the Data Science Community.
Sany Mathew graduated with a Masters in Information Systems Management with a specialization in Business Intelligence and Data Analytics from Carnegie Mellon University.