Sunstone Credit provides flexible financing solutions for businesses of all sizes, offering commercial solar loans with borrower-friendly terms and an efficient application process.

Sunstone’s Mission

We think climate change is the greatest issue facing our planet and we started Sunstone to do our part to fix it.

Electricity production is the single largest contributor to greenhouse gas emissions, so to tackle climate change and reach decarbonization goals, every segment of the solar market needs to thrive. However, small and medium sized businesses have been left behind by the recent boom in solar due to a lack of accessible financing options.  At Sunstone, our mission is to fix this problem and democratize access to solar for all businesses.

Our company is built on the three pillars of climate, technology and finance –each equally important –and our team reflects that.  We’ve sold and installed solar systems, founded and scaled climate tech businesses, developed technology for global companies and been bankers at the top firms. But most importantly, we are moms, dads and global citizens who want to have a lasting positive impact on the world...

...and we’re just getting started on our journey to finance the growth of the commercial solar market and drive a cleaner future for the planet.

Source: Sunstone Company Website

My Role.

For two consecutive summers (2023-2024), I interned at the Baltimore and New York City offices of Sunstone Credit.

I was part of a diverse and talented agile product team developing digital solutions for commercial solar loan applications. During my internship we launched multiple releases with enhanced features that increased customer satisfaction, and improved the application and approval process.

Financial models at Sunstone estimate the anticipated performance in terms of power generation and cost reductions for different small and midsize solar installations, which is then used to first, risk price the owner of that project, and second, determine whether Sunstone should finance the project and under what terms.

During the summer of 2023, I supported the leader of Digital Product Management, working on product features and product requirements, and separately worked with the head of Software Engineering mostly on small software tasks in Python to automate, and analyze testing. This past summer, I worked in Engineering as one of their software developers. We did agile development, had huddle meetings, and software architecture work sessions. I worked on different engineering tasks in the Sunstone` software stack, ranging from user interface design to QA testing.  I presented in front of the Engineering team on my own projects.

More broadly the combination of math, coding and engineering used to solve practical problems was a fascinating experience. The application into renewables was a huge plus; it put my passions to work in areas that can help with climate change. Working with a young team in a startup, and doing so under a female head of Engineering, added to the great experience.

My Responsibilities.

  • Coding.

    Programmed in Python new features and functions for the customer mobile app .

  • Project Management.

    Developed project plan for upcoming releases and managed all associated tasks. Leveraged agile methodology to set up and lead daily stand ups and regular showcases.

  • Digital Product Management.

    Using collaborative tools like Miro and Jira, developed user stories and user interfaces for the end customer mobile app.

  • Showcasing.

    Organized and presented the enhanced features and functionality in code and design showcases.

A Related Climate Analytics Project @ Andover.

Climate change and climate transition:

  • In case people have not noticed…October 2024 was pretty warm in the Northeast. In New York City, the average monthly temperature was the hottest in the past century!

  • The climate change challenge touches every aspect of our lives… from energy to transportation; from food and agriculture to steel, cement and infrastructure; from industry to our homes. 

  • We can be more efficient in our personal choices.

  • We can try to transition from traditional ways of doing things to more sustainable ways – my family switched to an EV last year, and we all like it. (Note: we live in NYC and I go to school outside of Boston that creates an apparent challenge in driving an EV back-n-forth, but it turns out it is very doable and efficient.)

  • Smart engineering solutions will help us avert the consequences of increased intensity natural disasters such as Storm Daniel in 2023 https://en.wikipedia.org/wiki/Storm_Daniel (I had just left Greece a week earlier), or hurricanes Helene https://en.wikipedia.org/wiki/Hurricane_Helene and Milton https://en.wikipedia.org/wiki/Hurricane_Milton   in 2024

  • AI is sure to unlock efficiencies, from how to control power consumption in our buildings to how to optimize transportation solutions, to help invent new solutions to help address climate change. It is also consuming a lot of electricity, and I am trying to read more about that topic.

An example: in my junior year CS / data science class I suggested (but did not get approval to do) a project to study the temperature variation inside and outside Andover buildings, and examine possible thermostat control policies that would try to reduce power consumption. Here is some detail:

  1. I had access to daily temperature data in Andover, MA, and specifically for each day I had the  noon temp (or avg temp, depending on the source), min temp (probably around 5am) and max temp (probably around 2pm). This is daily data, and I had reliable access to the past 30 to 50 years (depending on the source).

  2. I could, therefore, analyze the data to examine variability month-to-month and year-over-year with focus on Sept-May school year period. 

  3. Approximate hourly temperature profiles: I did not have historical data going back decades for hourly data for each day, but I had data for the past few years and certainly the current year. For each month and each day, I was planning to create an hourly temperature profile curve, and combine them to form a typical hourly profile for each month that I would then apply to create approximated hourly profiles for my datasets. Two missing pieces for my analysis that I needed to figure out were:

    • How to normalize these curves by their (min, max, avg) temperatures, so that I can then combine them.

    • How to apply the normalized profile to any specific day to achieve the right (min, max, avg) for that day while following that profile closely. 

  4. Power usage to heat the buildings is a function of the difference of the desired temperature inside the buildings, to the outside temperature, and depends on the insulation of the buildings. For example, if the target is to keep the building always at 72 degrees during the September to May months, I could compute the daily difference between the avg temp on that day and the target of 72 degrees, and take the absolute value. And, crudely, approximate the power consumption as the absolute value of that difference, allowing for heating and cooling, or try to focus only on the heating requirement on days where the outside average temperature was below the target temperature of 72 degrees. 

  5. I would repeat the daily power calculation using the hourly profiles, and summarize the data by month and examine year-over-year fluctuations.

  6. The last step would be to consider how to minimize the power usage by picking the target temperature. The simplest way would be to repeat the previous analysis by varying the target temperature and study the effect. And use the analysis to identify if 1 degree difference would have a significant effect on power consumption. I would hope to do the same analysis by setting different target daytime and overnight temperatures. 

There is a huge issue that I do not know how to study, which has to do with how building insulation affects the power consumption calculations, and how to customize the study to newer and older buildings that we have on our campus. 

I imagine that we should be able to use next day forecast information to change the daily target temperatures, but I did not find easy access to that historical forecast information stored for me to download. But I am sure it must exist and be available. Something to try in the summer…