We are Bright:

Bright is a consumer fintech that helps Americans get out of debt, with the power of data science and machine learning. It is a mobile app that combines all the tools and tech needed to manage and get rid of debt.

Bright’s tools include credit score building, automated debt paydown plans, financial planning, budget planning tools, and refinance loans. It works with credit cards, student loans and car loans.

Bright has had 6x growth in the last year, with 300,000 users, and more than 100,000 ratings and reviews.

Bright is backed by three major venture capital funds (Sequoia, Falcon Edge and Hummingbird) and with top angel investors from the US, UK and India, Bright has raised +$40 million in funding to date.

Bright has recently raised $50M in debt funding from Encina Lender Finance, for its credit business growth. Encina Lender Finance provides lending solutions to consumer and commercial speciality finance companies across the U.S. and Canada.

Today we are among the top 8 US FinTech companies. We will become a top-100 US financial institution, with the unique strength of data science and predictive modelling to enhance financial products for a user’s life outcomes.

We will be the first at-scale Consumer Tech company, built in India for Global markets.

About Our Founders:

Bright was founded in 2019 by a founding team from McKinsey’s Banking Practice (Petko Plachkov and Avi Patchava) and InMobi Data Scientist (Avi Patchava, Varun Modi, Avinash Ramakath, Jayashree Merwade).

Responsibilities:

Assist in collecting, cleaning, and preprocessing data from various sources to ensure data quality and integrity.
Perform exploratory data analysis to uncover insights and trends that inform business decisions.
Support the development and implementation of machine learning algorithms and predictive models.
Collaborate with cross-functional teams to understand project requirements.
Assist in the creation of data visualizations and reports to communicate findings to stakeholders.
Stay updated on the latest advancements and best practices in data science and contribute innovative ideas to ongoing projects.

Requirements:

Pursuing/Completed a degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
Strong understanding of machine learning algorithms and techniques.
Strong proficiency in programming languages such as Python or R.
Familiarity with data manipulation and analysis libraries/packages (e.g., Pandas, NumPy, Scipy).
Strong understanding of statistical concepts and techniques.
Ability to work in a dynamic and fast paced team environment.

Preferred Qualifications:

Knowledge of SQL or other database querying languages.
Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.
Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.
Familiarity with Gen AI technologies is a plus