Data Science III

About Bright Money

Bright Money is a Consumer FinTech for middle-income consumers. It brings together SaaS products to help consumers build credit, manage their debt, and get access to suitable loans and credit cards. These are the major financial needs of middle-income consumers living paycheck-to-paycheck.
Bright is a profitable, India-built, U.S.-market Consumer FinTech, with revenue trajectory to $100M in the coming 6 months. The business model combines subscription revenue with a financial service marketplace revenues, for loans, credit cards, and credit building tools. 

Bright has launched an AI money assistant and AI agentic UX flows for access to credit. It will be the first AI-driven Consumer FinTech for middle-income consumers.

The Bright product suite has potential for global markets beyond the US. The market size is 100M global middle-income consumers, with a revenue pool opportunity of $15B.

Bright is backed by 3 top global venture capital firms, and leading tech angel investors in the USA, Europe, and Asia.

Bright was founded in 2019 by a founding team from McKinsey’s Banking Practice (Petko Plachkov and Avi Patchava) and InMobi Data Scientists (Varun Modi and Avi Patchava). The core team is ex-InMobi Engineering and Data Sciences, recently joined by senior leaders from McKinsey. Bright is a data-driven, fast-paced execution culture that will build a Global Consumer FinTech, all functions operating from India.

What is the team & role about?

The Data Scientist – Level 3 is responsible for leading predictive modelling and experimentation to drive key business metrics around risk and impact. The role combines hands‐on modelling, AI agents/ML systems development, and stakeholder collaboration to turn complex data into actionable strategies. This is an IC role while mentoring juniors.

What you will do?

Own end‐to‐end predictive modelling initiatives for business‐critical metrics related to risk, impact, and growth, including problem framing, data strategy, modelling, and deployment.

• Design and execute segmentation, underwriting, and matching algorithms to improve customer targeting, pricing, and decision automation.

• Lead experimentation and post‐modelling evaluation, including A/B tests, back‐tests, and lift analyses, ensuring robust, statistically sound results.

• Build, train, and iteratively improve ML models (supervised, unsupervised, and forecasting) using modern libraries and MLOps practices.

• Develop and train AI agents or decisioning systems that integrate with products and workflows to automate and optimize business processes.

• Translate ambiguous business or product problems into clear analytical problem statements, hypotheses, and measurable success metrics.

• Partner with product, engineering, risk, and operations to embed models into production systems and monitor performance over time.

• Communicate insights, trade‐offs, and recommendations clearly to senior stakeholders through dashboards, presentations, and concise written summaries.

• Mentor junior data scientists and analysts, promoting best practices in experimentation, coding standards, and model governance.

• Strong fundamentals in statistics and probability with demonstrated experience in mathematical and statistical modelling for real‐world problems.

• Proven ability to build and design ML algorithms, including feature engineering, model selection, hyper‐parameter tuning, and performance optimization.

• Hands‐on implementation skills with basic understanding of software/Data engineering concepts (version control, testing, modular code, CI/CD for models).

• Expertise in Python (or similar), SQL, and modern data science tooling (e.g., Jupyter, pandas, scikit‐learn; exposure to Spark or cloud platforms is a plus).

• Experience with segmentation, matching, recommendation, or risk/underwriting models in production environments Ability to independently research, evaluate, and apply state‐of‐the‐art statistical/ML/AI techniques to new problem domains.

• Strong business problem translation skills: comfort working with incomplete information and iteratively refining scope with stakeholders.

• Excellent communication skills with the ability to present complex analyses simply and persuasively to technical and non‐technical audiences.

Ideal candidate profile looks like?’

• 4–7 years of relevant experience in data science, machine learning, predictive modeling, or applied statistics, with a track record of owning high‐impact projects end‐to‐end.

• Master’s degree (or higher) in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative discipline, or a related quantitative discipline from a Tier-1 institution.

• Prior experience in a Data Scientist role or equivalent responsibility in a high‐growth or product‐led organization.

• Strong foundations in statistics and probability, with hands-on experience applying mathematical and statistical modeling to real-world business problems.

• Proven experience designing and building machine learning models, including feature engineering, model selection, hyperparameter tuning, validation, and performance optimization.

• Strong proficiency in Python, SQL, and modern data science tools such as Jupyter, pandas, and scikit-learn. Exposure to Spark, cloud platforms, or MLOps tools would be an advantage.

• Experience developing and deploying segmentation, matching, recommendation, risk, or underwriting models in production environments.

• Working knowledge of software and data engineering practices, including version control, testing, modular coding, model deployment, and CI/CD.

• Ability to independently research, evaluate, and apply emerging statistical, machine learning, and AI techniques to new problem areas.

• Strong business problem-solving skills, with the ability to work with incomplete information and translate ambiguous business requirements into measurable analytical problems.

• Excellent communication and stakeholder-management skills, with the ability to present complex analyses and recommendations clearly to technical and non-technical audiences.

• Ability to mentor junior data scientists and analysts while continuing to operate as a hands-on individual contributor.

What Bright Offers?

• A profitable sub-unicorn with U.S. market scale – liquidity potential at $1–3B valuations.

• Real ownership: You run your own BU with an independent P&L.

• Wealth creation: Equity at an inflection stage of scale.

• A data-driven, high-cadence culture that combines consulting rigor with FinTech speed.