About the Role

We are looking for a hands-on Data Scientist with strong expertise in recommendation systems, classical Machine Learning, and Deep Learning.

You will work on complex ML problems involving large-scale, multimodal, and partially unlabeled data, including text, images, signals, and tabular datasets. The role requires strong modeling depth and the ability to own the full ML lifecycle — from exploratory data analysis and data validation to model development, evaluation, and production deployment.

This position is ideal for someone who understands not only how to use ML frameworks and libraries, but also the mathematical and algorithmic principles behind the models they build.

What You’ll Do

• Design, develop, validate, and deploy Machine Learning and Deep Learning models end-to-end
• Build and improve recommendation systems focused on user, product, and content similarity
• Develop approaches for similarity matching across heterogeneous and multimodal data
• Work with large volumes of structured and unstructured data, including text, documents, images, signals, and tabular datasets
• Perform exploratory data analysis and validate the quality and suitability of training data
• Translate business challenges into practical ML solutions and modeling strategies
• Develop solutions for datasets with limited or missing labels
• Explore advanced approaches such as contrastive learning, embeddings, pseudo-labeling, and representation learning
• Design and evaluate similarity metrics when standard distance functions are not sufficient
• Build production-quality data pipelines and model implementations
• Fine-tune and adapt pretrained models for domain-specific use cases
• Collaborate with engineering and business stakeholders to bring models into production

What We’re Looking For

Machine Learning & Data Science

• Strong foundation in classical Machine Learning, statistics, and mathematics
• Deep hands-on experience developing ML models, not only designing high-level architectures
• Experience with supervised, unsupervised, and transfer learning
• Strong understanding of recommendation systems and similarity-based modeling
• Experience with embeddings, representation learning, and similarity search
• Ability to select and justify modeling approaches based on business and data constraints
• Experience with time series analysis, including at least one approach beyond standard off-the-shelf solutions

Deep Learning

• Strong understanding of neural network architecture and internal mechanics
• Practical experience with CNNs and Transformers
• Understanding of layer design choices and their impact on model behavior
• Strong knowledge of loss functions and when to use different approaches
• Understanding of activation functions, gradient flow, and common edge cases such as dying ReLU
• Experience with computer vision, pretrained models, and fine-tuning
• Familiarity with techniques such as contrastive learning is highly desirable

Data & Algorithms

• Experience validating training data quality and defining data validation methodologies
• Experience working with unstructured data such as documents, PDFs, images, and text
• Strong algorithmic and problem-solving skills
• Solid understanding of classical algorithms, including dynamic programming, greedy approaches, and optimization problems
• Ability to work with large-scale datasets of 100GB+ in cloud environments

Engineering & Cloud

• Strong Python skills and ability to write clean, production-quality code
• Experience building data pipelines and implementing ML solutions in production
• Hands-on experience with GCP or Azure; experience with both is a plus
• Familiarity with MLOps practices, deployment, and model monitoring is an advantage

Nice to Have

• Experience with multimodal Machine Learning
• Experience working with unlabeled or weakly labeled datasets
• Knowledge of pseudo-labeling and advanced representation learning techniques
• Experience with large-scale recommendation engines
• Familiarity with production ML infrastructure and MLOps tooling