BILL is looking to leverage advanced analytics, machine learning, and AI to drive business insights and deliver innovative solutions that empower their customers, aiming to shape the future of financial operations.
Requirements
- Proven experience applying data science, machine learning, and AI to real business problems end-to-end (scoping → analysis/modeling → insights → stakeholder adoption).
- Proficiency in SQL and Python, including data wrangling, feature engineering, and visualization with modern libraries and tools.
- Depth in model selection and evaluation, with hands-on use of appropriate metrics and validation techniques; ability to diagnose under/overfitting and communicate limitations.
- Experience with experimentation and causal methods applied to product growth or revenue outcomes (e.g., conversion, upsell, churn, CLTV).
Responsibilities
- Analyze large, complex datasets (transactional, behavioral, and firmographic) to extract actionable insights that inform the Product roadmap to improve conversion, cross-sell / upsell, and retention.
- Develop, operationalize, and maintain predictive ML pipelines with rigorous EDA, feature engineering and model training. Infer, deploy and productionalize ML model outputs while implementing iterative feedback loops.
- Collaborate with product, engineering, sales/revenue, and analytics partners to identify opportunities for data-driven solutions and operationalize human-in-the-loop workflows
- Communicate findings, trade-offs, and limitations clearly to both technical and non-technical audiences; create dashboards and slides that are actively used for decision-making.
- Design and analyze experiments (A/B, holdouts, quasi-experimentation methods) to validate hypotheses and quantify impact on key outcomes with statistical rigor
- Build and evaluate causal models for CLTV/revenue, selecting fit-for-purpose metrics (e.g., ROC-AUC, precision/recall, RMSE) and robust validation.
- Deploy and/or integrate AI tools that enhance customer insights and engagement, and contribute to improving data confidence and automation in analytics workflows.
Other
- Collaborate effectively in cross-functional teams.
- Mentor junior team members and share best practices in EDA, model evaluation, and storytelling with data.
- Excellent communication skills with the ability to tailor messages to diverse audiences.
