Data Scientist

Job Description

Location
Remote in Europe.

Albatross
At Albatross, we’re building the second pillar of AI: a perception layer that understands how users actually experience content, in real time. Trained on live user interactions, Albatross learns and reasons on the fly. Our technology powers real-time, in-session discovery  by adapting to evolving user interests, in real-time. We have raised significant funding and our platform already operates at scale, with billions of events being processed and hundreds of millions of predictions served.

The Role
As a Data Scientist, you will design and deploy machine learning models that power real-time personalization for our customers. You will own defined workstreams of ML projects end-to-end, and you will work closely with Applied Scientists and Engineers to translate product and customer needs into scalable ML solutions. More specifically, you will:

• Design and implement machine learning models for ranking, recommendation, and personalization.
• Define feature engineering pipelines and modeling strategies for customer use cases.
• Train, evaluate, and deploy models using our internal ML tooling and infrastructure.
• Own project workstreams from data preparation through production deployment.
• Collaborate with Applied Scientists to integrate new algorithms into production systems.
• Contribute improvements to internal ML tooling and experimentation infrastructure.
• Monitor model performance and iterate based on real-world feedback.

Requirements

• Bachelor's degree in Machine Learning or STEM.
• Strong background in machine learning, statistics, or data science.
• Solid programming skills in Python.
• Experience training and deploying ML models in production environments.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience working with large-scale datasets and feature engineering pipelines.
• Ability to work independently on moderately complex ML problems.
• Strong communication skills in English.

Nice to Have

• Experience with recommender systems, ranking models, or search.
• Experience with large-scale experimentation and evaluation pipelines.
• Familiarity with learning-to-rank models, bandits, or reinforcement learning.
• Experience working with cloud environments such as AWS, GCP, or Azure.

Benefits

• Flexibility to work from anywhere across Europe.
• Budget for learning and training, attend events and conferences.