Senior Data Scientist, Banxware

• 5+ years experience in applied data science, machine learning, or analytics with demonstrable end-to-end project ownership
• Strong experience in business analytics (churn, LTV, sales/marketing analytics, forecasting, experimentation)
• Proven track record deploying machine learning models into production using MLOps practices (CI/CD, model serving, monitoring)
• Knowledge implementing LLM-based solutions in production (prompting, fine-tuning, RAG architectures, safety/guardrails)
• Hands-on with common ML/AI stack: Python, scikit-learn, TensorFlow/PyTorch, experience with containerization (Docker) and orchestration
• Solid SQL skills and experience working with production data platforms (cloud data warehouses, data lakes)
• Familiarity with model governance, testing strategies, and performance monitoring
• Ability to translate analytic results into business recommendations and influence senior stakeholders

Desirable

• We’re hiring a Senior Data Scientist to lead analytics-driven decisioning across core business functions. This role has three primary focuses: business analytics and strategic insight, risk and portfolio analytics (last two highly desirable but not necessary), and building production-grade ML/AI solutions — including MLOps and LLM-based features
• You’ll partner closely with our Risk-focused data science team and engineering partners to turn complex data into reliable, scalable AI products that drive measurable business outcomes
• Lead end-to-end analytics and modeling projects that deliver actionable business insights for stakeholders (commercial, pricing, operations, credit, etc.)
• Design, develop, validate, and deploy advanced statistical models and machine learning solutions for business metrics (propensity, churn, forecasting, CLTV, segmentation, etc.)
• Support building and productionizing models and AI systems using modern MLOps practices: CI/CD for ML, model versioning, monitoring, automated retraining, and scalable serving
• Architect and implement LLM and other generative AI solutions safely and effectively in production (prompt engineering, fine-tuning/adapter strategies, retrieval-augmented generation, hallucination mitigation, guardrails)
• Collaborate with the Risk & Portfolio analytics team to extend models for risk assessment, portfolio health, stress testing, and provisioning. Translate risk requirements into robust model assumptions and validation steps
• Establish and maintain model governance, documentation, testing, and observability aligned with enterprise standards and regulatory expectations
• Upskill Banxware’s data capabilities including other data scientists and engineers
• Communicate technical findings and trade-offs clearly to senior stakeholders and non-technical partners; recommend measurable business actions
• Lead pilots and proofs-of-concept; convert successful pilots into production-grade systems in partnership with engineering teams