The Data Science Difference at Lendio

As someone who has been a data scientist for two Fortune-100 companies and a startup, I have noticed two common problems that slowed my efforts more than anything else. One of them is prohibitive business practices that turn a 2-month project into a 1-year project. Another is not being allowed to use the top of the line tools and platforms that optimize a data scientist’s time. Lendio DOES NOT have these issues.

Every business touts their ability to stay agile and handle change but many aren’t actually equipped to do this in an effective way. In my time at Lendio, instead of being met with unending obstacles and needing sign-off from countless stakeholder leadership teams, I’ve been granted the datasets and buy-in needed to progress through my projects efficiently. 

Lendio can navigate the tenuous ground between giving their data scientists and developers enough time to do their job properly while still getting results. They accomplish this without prolonging their efforts with complications at each step and ensuring that instructions are clear. With each potential project that I have explored at Lendio, leadership teams have given full support to proper research to determine the benefit of the project. Regardless of whether the project is for a niched efficiency gain, or a broader stroke like a knowledge graph or macroeconomics index, leadership provides what it takes to get it right.

Synchrony happens when business leaders are educated on what data science is and, more importantly, what it isn’t. Product management teams do a great job at educating stakeholders on what to expect from the data science team which makes expectations of each party clear and efficient. As a data scientist at Lendio, I have had minimal difficulties getting buy-in from stakeholders on projects because they understand the value of my work. 

Lendio’s data infrastructure, product management, and development suite is as good as I have seen anywhere in my 6 years as a data scientist. The technologies that the team uses enables them to collaborate effectively, ensure that code is ideal in quality, and yield results that make meaningful impact to users of the models and analysis they provide. Lendio provides any technology that is needed to do this properly.

After searching for years, I have found a company that mitigates the issues that inhibit data scientists the most. This combination of ready buy-in, team efficiency, and a top-of-the-line technology stack give Lendio the ideal culture for data science and machine learning efforts to prevail.