As a Machine Learning Engineer in the CS Forecasting team, you will be responsible for the deployment, optimization, and maintenance of forecasting models used to produce forecasts for the CS organization. You will also be responsible for maintaining the integration of models into production environments, as well as monitoring product health, performance, and business impact. You will collaborate closely with the machine learning scientists in the team to translate business requirements into robust machine learning solutions.
Key Responsibilities
Develop production-grade machine learning code, from models to features and pipelines, allowing for scalability, realtime, monitoring and retraining.
Build readable and reusable code, choosing the right technologies and coding methodologies applying knowledge of business area tools and product needs.
Monitor product health, performance and business impact and act accordingly when requirements are not met.
Identify underlying issues and opportunities across related domains and situations through application of structured thinking and logic.
Solve issues by applying methods and insights gained from a variety of disciplines, navigating a variety of environments.
Clearly communicates with stakeholders at all levels when appropriate.
Continuously evolve their craft, keeping up to date with the latest technologies.
Actively contribute to Machine Learning at Booking.com through training, exploration of new technologies, interviewing, onboarding and mentoring colleagues.
Collaborate with central teams to improve, scale and extend machine learning tooling and infrastructure.
Responsible for technical implementation and maintenance of data processing services and storage systems in line with the Data Governance Framework
Identify and research opportunities to optimize our forecasting models, as well as the input and output data.
Explore and combine different forecasting methodologies, both based on historical time series as well as driver based forecasting.
Ensure a robust, stable and reliable data pipeline for input data, which is as close as possible to the source data.
Engage with stakeholders and end-users of our forecasts to ensure that the product is optimally fit for purpose and to stay ahead of future departmental developments.
Requirements of special knowledge/skills
4+ years of relevant work experience (or equivalent), involved with the application of Machine Learning to business problems, preferably in a commercial environment.
Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks.
Experience with data at scale using MySQL, Pyspark, Snowflake and similar frameworks.
Demonstrable experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.
Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib.
Deep understanding of machine learning algorithms, statistical models, and data structures.
BsC or higher in Computer Science, Artificial Intelligence, Software Engineering, or related fields.
Knowledge of multiple machine learning facets, such as working with large data sets, experimentation, scalability and optimization.
Experience with data-driven product development: analytics, A/B testing, etc.
Strong experience in one or more general purpose programming languages, preferably python
Knowledge of version control systems.
Excellent English communication skills, both written and verbal.
Experience in time series data and models and familiarity with cloud-native ML services (AWS SageMaker, GCP Vertex AI, Azure ML)- an advantage
Booking.com’s Total Rewards Philosophy is not only about compensation but also about benefits. We offer a competitive , as well unique-to-Booking.com benefits which include:
- Annual paid time off and generous paid leave scheme including: parental (22-weeks paid leave), grandparent, bereavement, and care leave
- Hybrid working including flexible working arrangements, working from home furniture and ergonomic support, and up to 20 days per year working from abroad (home country)
- A beautiful sustainable , that offers on-site meals, coffee, and snacks, multi-faith and breastfeeding rooms at the office
- Commuting allowance and bike reimbursement scheme
- Discounts & Wallet credits to spend on our products, upgrade to Booking.com Genius Level 3, and friends & family Booking.com discount vouchers
- Free access to online learning platforms, development and mentorship programs
- Global Employee Assistance Program, free Headspace membership
Career Development Opportunities:
- Learn more about here.
- This role does not come with relocation assistance.
Pre-Employment Screening
If your application is successful, your personal data may be used for a pre-employment screening check by a third party as permitted by applicable law. Depending on the vacancy and applicable law, a pre-employment screening may include employment history, education and other information (such as media information) that may be necessary for determining your qualifications and suitability for the position.
