Duetto
About Duetto
Duetto delivers a suite of SaaS cloud‑native applications that let hospitality businesses optimize every booking opportunity for greater revenue impact. Duetto’s platform provides real‑time dynamic data sources and actionable insights into pricing and demand across the enterprise. For more information, visit
https://www.duettocloud.com .
Lead Data Scientist – Dynamic Pricing We are seeking a Lead Data Scientist with deep expertise in pricing science, including reinforcement learning and mathematical optimization, to drive the next generation of our dynamic pricing engine.
In this role, you will lead the development of ML systems that learn optimal pricing strategies for thousands of hotels, dynamically adapting to demand signals, business constraints, and market changes.
You’ll collaborate closely with engineering and product teams to design models that move beyond our legacy pricing approaches—enabling context‑aware, self‑learning price policies that deliver measurable revenue impact. This is an opportunity for a hands‑on, full‑stack data scientist who thrives in ambiguity, has strong modeling intuition, and is energized by the challenge of building intelligent systems at scale in a complex, real‑world domain.
Key Responsibilities
Lead the development of intelligent pricing systems using reinforcement learning, constrained optimization, and simulation‑based approaches.
Apply advanced reinforcement learning algorithms (e.g., model‑free, policy gradients, model‑based RL) combined with mathematical optimization and dynamic programming to develop adaptive pricing strategies at scale.
Incorporate price elasticity modeling, demand forecasts, and business constraints into the optimization framework.
Build and validate simulation environments to evaluate policies offline and ensure robustness before production deployment.
Collaborate with engineering to productionize and monitor models in cloud environments such as AWS SageMaker.
Partner with product, pricing, and revenue strategy teams to define objectives and translate pricing insights into business outcomes.
Define and execute model performance measurement strategies, including causal inference, uplift modeling, and A/B testing.
Present findings, experimental results, and strategic recommendations to senior leadership.
Qualifications
MS or PhD
in Statistics, Econometrics, Computer Science, Operations Research, or a related quantitative field.
7+ years
of hands‑on experience building data science solutions for pricing, decision‑making, or control systems.
Demonstrated success applying reinforcement learning in production settings, ideally in pricing or dynamic decision environments.
Proficiency with ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit‑learn, DARTS) and programming languages (Python, R, SQL).
Experience with Python‑based libraries for mathematical optimization and reinforcement learning, and familiarity with simulation environments for offline policy evaluation.
Familiarity with cloud platforms and MLOps tools (e.g., AWS SageMaker, MLflow) for scalable model development and deployment.
Strong communication and presentation skills, capable of conveying complex analytical concepts to non‑technical stakeholders.
Prior experience in the hospitality, travel, or revenue management domain is highly desirable.
Referrals increase your chances of interviewing at Duetto by 2x
Austin, TX
#J-18808-Ljbffr
https://www.duettocloud.com .
Lead Data Scientist – Dynamic Pricing We are seeking a Lead Data Scientist with deep expertise in pricing science, including reinforcement learning and mathematical optimization, to drive the next generation of our dynamic pricing engine.
In this role, you will lead the development of ML systems that learn optimal pricing strategies for thousands of hotels, dynamically adapting to demand signals, business constraints, and market changes.
You’ll collaborate closely with engineering and product teams to design models that move beyond our legacy pricing approaches—enabling context‑aware, self‑learning price policies that deliver measurable revenue impact. This is an opportunity for a hands‑on, full‑stack data scientist who thrives in ambiguity, has strong modeling intuition, and is energized by the challenge of building intelligent systems at scale in a complex, real‑world domain.
Key Responsibilities
Lead the development of intelligent pricing systems using reinforcement learning, constrained optimization, and simulation‑based approaches.
Apply advanced reinforcement learning algorithms (e.g., model‑free, policy gradients, model‑based RL) combined with mathematical optimization and dynamic programming to develop adaptive pricing strategies at scale.
Incorporate price elasticity modeling, demand forecasts, and business constraints into the optimization framework.
Build and validate simulation environments to evaluate policies offline and ensure robustness before production deployment.
Collaborate with engineering to productionize and monitor models in cloud environments such as AWS SageMaker.
Partner with product, pricing, and revenue strategy teams to define objectives and translate pricing insights into business outcomes.
Define and execute model performance measurement strategies, including causal inference, uplift modeling, and A/B testing.
Present findings, experimental results, and strategic recommendations to senior leadership.
Qualifications
MS or PhD
in Statistics, Econometrics, Computer Science, Operations Research, or a related quantitative field.
7+ years
of hands‑on experience building data science solutions for pricing, decision‑making, or control systems.
Demonstrated success applying reinforcement learning in production settings, ideally in pricing or dynamic decision environments.
Proficiency with ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit‑learn, DARTS) and programming languages (Python, R, SQL).
Experience with Python‑based libraries for mathematical optimization and reinforcement learning, and familiarity with simulation environments for offline policy evaluation.
Familiarity with cloud platforms and MLOps tools (e.g., AWS SageMaker, MLflow) for scalable model development and deployment.
Strong communication and presentation skills, capable of conveying complex analytical concepts to non‑technical stakeholders.
Prior experience in the hospitality, travel, or revenue management domain is highly desirable.
Referrals increase your chances of interviewing at Duetto by 2x
Austin, TX
#J-18808-Ljbffr