Senior Data Scientist (Search)

About the role

This Senior Data Scientist position involves taking ownership of machine learning projects from their initial problem definition through to deployment and ongoing support. The work centers on enhancing search relevance and developing new features for online travel booking platforms. Key responsibilities include collaborating with data engineers to define data requirements and assess project feasibility, as well as working with data analysts on A/B test design, interpretation, and recommendations.

The role requires developing and training both classic machine learning and deep learning models, including solutions for text and image embeddings, along with conducting offline evaluation and error analysis. After deployment, the successful candidate will be responsible for the model's quality, monitoring metrics, detecting drift or degradation, and planning improvements. This includes ensuring the Python service is production-grade, with readable code and tests for critical components.

Emerging Travel Group is a global travel-tech company operating since 2010, focused on creating convenient travel products. The team is described as ambitious and supportive, emphasizing mutual appreciation and growth. This role offers a remote work setup with flexible schedules and opportunities for professional development through internal programs, external training compensation, and corporate English lessons.

The salary for this position ranges from $115,000 to $195,500 USD.

Skills that matter here

• Python: Used for developing and deploying production-grade code, including model/artifact packaging and service integration.
• ML: Applied to manage end-to-end projects, including feature engineering, boosting, classification/regression, cross-validation, and model interpretability.
• DL: Utilized for developing and training models, including solutions for text and image embeddings, with an understanding of fine-tuning and inference.
• SQL: Required for independent dataset building, including joins and window functions.
• PyTorch: One of the deep learning frameworks used for model development and training.
• MLflow: One of the experiment tracking tools used for managing machine learning workflows.

Who this role suits

• A person with at least four years of data science experience, specifically in search, ranking, or recommendations tasks.
• Someone who thrives on managing machine learning projects from conception to production support.
• An individual who understands classical machine learning principles and deep learning frameworks, capable of writing production-grade Python code.
• A candidate who values collaboration with data engineers and analysts to ensure successful project delivery and model quality.

From the employer

• Manage end-to-end ML projects: problem definition → solution → testing → deployment → support.
• Work with data engineers to build datasets and define data requirements, and assess feasibility, risks, and constraints.
• Work with data analysts to design and analyze A/B tests: metrics, splits, interpretation of results, and recommendations for deploying solutions.
• Develop and train models (classic ML + DL), including solutions for text and image embeddings; conduct offline evaluation and error analysis.
• Deploy the model and code to production (Python service), support releases and integrations.
• Be responsible for model quality post-launch: metrics, monitoring, drift/degradation, improvement plans, and support procedures.

• 4+ years of experience as a Data Scientist (with specific experience in search / ranking / recommendations tasks).
• Experience managing end-to-end ML projects in production (from setup to support).
• Excellent understanding of classical ML: feature engineering, boosting, classification/regression, cross-validation, threshold selection, calibration.
• Experience with DL (PyTorch/TensorFlow): understanding of fine-tuning principles and model inference.
• Python (production-grade): readable code, tests for critical components, understanding of model/artifact packaging and service integration.
• Understanding of ML monitoring: quality metrics, drift, alerts, diagnostics, and support procedures.
• SQL proficiency sufficient for independent dataset building (joins, window functions).
• Experience with model interpretability and error analysis.
• MLflow / W&B / DVC or similar experiment tracking tools.
• Orchestration/pipelines (Airflow/Prefect/Dagster) and advanced data processing.

• Flexible schedules and opportunity to work remotely;
• Ambitious and supportive team who love what they do, appreciate each other, and grow together;
• Internal programs for adaptation and training, development of soft skills, and leadership abilities;
• Partial compensation for participating in external training and conferences;
• Corporate English school: Group and individual lessons, speaking clubs with colleagues from all over the world;
• Corporate prices on hotels and travel services;
• MyTime Day Off - an extra non-working day without loss of compensation.