Senior Data Scientist (Personalization)

At Fozzy Group, we are building the next generation of AI-powered personalization products that help millions of customers discover relevant products, simplify shopping, and create a seamless omnichannel experience.
 

As a Senior Data Scientist, you will own the design and delivery of machine learning solutions that power personalized customer experiences across our digital platforms. You will architect, build, and optimize large-scale recommendation systems end to end — from research and prototyping to production and measurable business impact on customer engagement, conversion, basket growth, and retention.

You will drive a broad portfolio of personalization products, including Recommendations, Similar Products, Product Substitutions, Bundles, Cross-sell, Upsell, and Next Best Action, partnering closely with Product Managers and Software Engineering teams, and mentoring less experienced data scientists along the way.
 

Key Responsibilities

• Own the end-to-end design, development, and deployment of large-scale recommendation systems for personalized product discovery.
• Own execution of personalization strategy across multiple customer touchpoints, including Home Page, Product Detail Pages, Basket, Search, Checkout, CRM campaigns, and other digital experiences.
• Architect ranking models that optimize recommendations across multiple, often competing business objectives: relevance, conversion, diversity, novelty, customer satisfaction, and revenue.
• Design customer and product representations using embeddings, representation learning, and graph-based machine learning techniques.
• Lead the development of customer preference and Next Best Action models using behavioral, transactional, and contextual data.
• Define experimentation strategy: design, execute, and analyze A/B tests to rigorously evaluate the business impact of personalization initiatives, applying causal inference where appropriate.
• Drive continuous improvement of recommendation quality using collaborative filtering, content-based methods, hybrid recommenders, learning-to-rank, and deep learning approaches — choosing the right method for each problem.
• Set standards for scalable feature engineering pipelines and reusable ML components, and drive their adoption across the team.
• Establish best practices for deploying, monitoring, and maintaining ML models in production, ensuring reliability, scalability, and reproducibility.
• Partner with Product, Engineering, Commercial, and Analytics leadership to translate business challenges into a roadmap of scalable AI products.
• Mentor middle and junior data scientists through code and design reviews, technical guidance, and knowledge sharing.
   

Preferred Qualifications

• 5+ years of experience in Data Science / Machine Learning, including proven experience building production-grade Recommendation Systems at scale.
• Deep understanding of Learning-to-Rank algorithms, ranking optimization, and multi-objective optimization.
• Expert-level knowledge of Collaborative Filtering, Content-Based, and Hybrid Recommendation models, and when to apply each.
• Strong background in customer modeling, preference learning, and representation learning.
• Experience with Graph Machine Learning or Graph Neural Networks is a plus.
• Advanced hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Proven track record of building scalable ML pipelines and production MLOps workflows.
• Strong expertise in experimentation, A/B testing, and causal inference, including experiment design for complex product ecosystems.
• Excellent SQL and Python skills; experience with Spark or distributed data processing frameworks is a plus.
• Demonstrated ability to lead technical initiatives, influence cross-functional stakeholders, and mentor other data scientists.
   

What We Offer 

• Competitive salary.
• Professional & personal development opportunities.
• Being part of dynamic team of young & ambitious professionals.
• Corporate discounts for sport clubs and language courses.
• Medical insurance package.