Data Scientist - Revenue Forecast

About us

, data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make.

Through our products, partners and people, we make it easier for everyone to experience the world.

The Financial Planning & Analysis team acts as the analytical engine of the finance organization, providing critical insights and recommendations that shape a company’s financial health and strategic direction. As a Machine Learning Scientist, you will be part of the “Forecast of the Future” program which will be responsible for implementing ML based statistical forecasting models at the core of our financial forecasting & budgeting process, with the aim to make our forecasts more agile, accurate, scenario-based and connected (driver-based and multi-vertical).

We are seeking an experienced Machine Learning Scientist to join our Financial Planning & Analysis (FP&A) team, focusing on advanced forecasting initiatives. In this role, you will design, develop, deploy, and maintain machine learning solutions—primarily in the areas of time series forecasting and regression modeling—to drive data-driven financial planning and decision-making. You will collaborate with business stakeholders to identify key features for enhancing forecasting accuracy.

You will also be responsible for feature exploration and conducting successful experiments to guide the company toward driver-focused forecasting.

• Work in a multi-disciplined, highly skilled team where you will be developing, implementing, and optimizing time series forecasting and regression models for financial planning, budgeting, and scenario analysis

• Collaborate with FP&A analysts, data scientists, and business stakeholders to understand forecasting requirements and translate them into scalable ML solutions.

• Leverage AWS Sage Maker for model development, training, deployment, and lifecycle management, including use of Data Wrangler, Model Registry and Feature Store

• Monitor model performance, detect model drift, and implement retraining strategies to maintain accuracy and relevance over time

• Conduct data analysis with detailed metrics to improve data quality, do feature exploration, evaluate model’s performance, etc.

• Ensure data quality, feature engineering, and preprocessing of large, complex financial datasets

• Drive a culture of quality for all ML practitioners, maintain data security, integrity and quality, and make choices over the right technologies and coding methodologies

• Identify underlying issues and opportunities across the ML development cycle and introduce tools to be used within and outside the team for efficient development and testing

• Continuously evolve your craft, keeping up to date with the latest technologies

• Collaborate with relevant ML teams across the company to improve, scale and extend machine learning tooling and infrastructure

• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related field

• 3+ years of relevant work experience, involved with the application of Machine Learning algorithms & statistical forecasting to business problems in a commercial environment

• Strong skills in coding and problem solving. Proficiency in at least one programming language; preferably Python and strong command of SQL is strictly required. Knowledge of Snowflake is a plus

• Proven experience building and deploying forecasting models (time series, regression) in a financial or business analytics context

• Experience running Machine Learning systems in production is a must, with knowledge on how to generate predictions for ML frameworks with high throughput

• Solid understanding of cloud platforms, containerization, and version control (e.g., Docker, Git)

• Demonstrable experience of multiple machine learning facets, such as working with large data sets, experimentation, scalability and optimization

• Profic…