Lead Data Scientist, Insurance

Role Overview

Traveloka's insurance sector integrates travel intent with risk evaluation, focusing on offering the right protection at optimal pricing during booking moments for flights, hotels, or activities. This position entails leading pricing model development and management for insurance cross-sell products, delivering live, on-demand pricing across various markets and enabling informed strategy testing.

Key Responsibilities

• Lead end-to-end insurance pricing initiatives, collaborating closely with product managers, data analysts, and engineers to translate business challenges into dynamic, data-informed pricing models with clearly defined KPIs.
• Develop and refine pricing models by preparing market-specific training datasets through SQL, performing exploratory data analysis to guide feature engineering, and routinely training and tuning models.
• Conduct rigorous validation using pricing simulations and backtesting to evaluate model robustness, revenue implications, and identify potential failure modes before live deployment.
• Deploy models in production environments and ensure ongoing operational health, partnering with Data Engineering and ML Platform teams to extend inference capabilities for price experimentation and manage features such as deployment cycles, freshness, monitoring, and drift detection.
• Effectively communicate methodologies, compromises, and outcomes both to technical colleagues and non-technical leadership, providing actionable recommendations.
• Define and drive the strategic vision for pricing science within the organization, setting methodologies, standards, and guiding stakeholders.

Candidate Profile

• Minimum of six years’ experience in data science or applied machine learning, with proven ownership of complete ML problem lifecycles.
• Demonstrated capability managing models that serve real-time production traffic with considerations for rapid feature updates, fallback mechanisms, and error handling under strict latency constraints.
• Proficient in SQL and Python, experienced with common modeling techniques such as gradient boosting, regression, causal inference, and uplift modeling, with strong judgment to select the appropriate model complexity.
• Solid understanding of experimental design and causal inference including A/B testing, holdouts, and backtesting to accurately attribute performance gains.
• Technical engineering skills covering version control, continuous integration/deployment, containerization, and familiarity with cloud infrastructure and monitoring tools sufficient for independent operation alongside platform teams.
• Confidence in direct interaction with business stakeholders regarding commercial decisions around margin and pricing trade-offs.
• Self-directed in a solo data scientist role, adept at prioritizing and justifying chosen methodologies independently.
• Bachelor’s degree in a quantitative discipline required; Master’s degree or higher preferred.

Desirable Qualifications

• Prior hands-on experience in pricing or revenue optimization modeling, including dynamic pricing, price elasticity assessment, willingness-to-pay evaluation, discount strategies, or risk-adjusted pricing models, with insights into practical deployment challenges.
• Experience managing multi-market or multi-currency pricing structures, ideally within Southeast Asian markets.
• Familiarity with feature stores and real-time streaming data pipelines.