About Bright Money

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)

Overview

We are seeking a highly motivated Data Scientist I with a strong foundation in machine learning, statistics, and probability. The ideal candidate will be capable of developing and productionizing machine learning models to solve practical business problems.

Key Responsibilities

Apply fundamental concepts of machine learning, statistics, and probability to design and develop predictive models.
Collaborate with cross-functional teams (engineering, product, business) to transform business requirements into data-driven solutions.
Productionize ML models, ensuring smooth deployment, scalability, and monitoring in real-world settings.
Analyze large datasets using statistical and computational methods to extract actionable insights.
Evaluate, tune, and interpret models for performance and business relevance.
Document methodologies, workflows, and experiments for reproducibility and cross-team knowledge sharing.

Required Qualifications

Bachelor’s or master’s degree in a quantitative field (Computer Science, Statistics, Mathematics, or similar).
Strong understanding of ML/statistics/probability fundamentals (regression, classification, hypothesis testing, probability distributions, etc.).
Hands-on experience in one or more programming languages (Python, R, etc.) and ML libraries (scikit-learn, TensorFlow, PyTorch).
Knowledge of model deployment tools and frameworks (Docker, Flask/FastAPI, MLflow, etc.).
Ability to implement and deploy models in a production environment.
Familiarity with recent advancements in AI/ML (transformers, large language models, generative AI, etc.).
Experience with cloud platforms (AWS, GCP, Azure) is a plus.
Strong problem-solving, communication, and teamwork skills.

Preferred Skills

Experience with deep learning frameworks.
Exposure to MLOps practices (CI/CD for ML, monitoring and retraining pipelines).
Publications or side projects demonstrating innovative use of contemporary AI methods.

What You Get to Work On

Model user behavior patterns to solve high-impact, business-specific use cases.
Work on complex savings account transactions data, mining actionable and meaningful insights.
Extend and apply advanced deep learning, ML, and LLM-based models to production-scale problems.
Build predictive and prescriptive models that directly influence product strategy and customer experience.
Collaborate with product and engineering teams to integrate ML-driven intelligence into customer-facing features.
Experiment with cutting-edge AI techniques (generative AI, transformers, reinforcement learning) for real-world financial applications.