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?

- 5 to 8 years of relevant experience in data science, machine learning, or applied statistics, with a track record of owning high?impact projects end?to?end.

- Masters degree (or higher) in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative discipline, or equivalent practical experience.

- Prior experience in a Level?3 / Senior / IC3 data scientist role or equivalent responsibility in a high?growth or product?led organization.

What Bright Offers?

- A profitable sub-unicorn with U.S.-market scale liquidity potential at $13B 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.