Lead Data Scientist

Lead Data Scientist - Forecasting

Location: Remote - USA

$200,000 + equity

Company Overview:

We are working with a fast-growing, well-funded global technology company transforming the hospitality industry. They are at the forefront of creating innovative analytical solutions that help hotels optimize revenue and enhance decision-making. The company offers a collaborative, flexible, and casual work environment, valuing diversity and inclusion as key drivers of their success in leading the market with cutting-edge hospitality technology.

Position Overview:

Join as a Principal Data Scientist and take a leading role in developing advanced machine learning models that predict hotel demand and optimize pricing strategies.

As an experienced full-stack data scientist, you will spearhead the development of statistical models and machine-learning techniques that will empower hotels worldwide to make data-driven decisions. This role involves close collaboration with engineering and product teams to integrate scientific innovations into the company’s revenue management solutions.

Key Responsibilities:

• Lead the development of scientific solutions to forecast hotel demand and pricing using advanced statistical modeling and machine learning techniques, including classical time series models, neural networks, and hierarchical forecasting methods.
• Apply cutting-edge deep learning techniques to complex forecasting and pricing challenges.
• Lead projects and provide technical mentorship to junior data scientists.
• Collaborate with engineering teams to integrate machine learning models into the company’s products seamlessly.
• Work closely with product teams to translate model outputs into actionable business strategies.
• Present analytical findings and strategic recommendations to senior management.

Qualifications:

• Master’s degree or higher in Statistics, Econometrics, Operations Research, Computer Science, or a related field.
• 10+ years of experience delivering innovative, data-driven solutions in dynamic environments, with a proven track record of applying scientific methods to solve large-scale real-world problems.
• Strong statistical and machine learning expertise, focusing on time series forecasting and deep learning techniques.
• Experience developing and deploying machine learning models at scale using platforms such as AWS SageMaker or similar.
• Ability to discern when to apply simple versus complex solutions, with experience leading projects in startup or similarly fast-paced environments.
• Strong communication skills, with the ability to convey complex analytical concepts to non-technical stakeholders.
• Proficiency in ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit-learn, DARTS) and programming languages (Python, R, SQL).