Data scientist with Java expertise

The primary goal of the project is the modernization, maintenance and development of an eCommerce platform for a big US-based retail company, serving millions of omnichannel customers each week.
Solutions are delivered by several Product Teams focused on different domains - Customer, Loyalty, Search and Browse, Data Integration, Cart.
Current overriding priorities are new brands onboarding, re-architecture, database migrations, migration of microservices to a unified cloud-native solution without any disruption to business.

• Responsibilities:

  We are looking for an experienced Data Engineer with Machine Learning expertise and good understanding of search engines, to work on the following:
  - Design, develop, and optimize semantic and vector-based search solutions leveraging Lucene/Solr and modern embeddings.
  - Apply machine learning, deep learning, and natural language processing techniques to improve search relevance and ranking.
  - Develop scalable data pipelines and APIs for indexing, retrieval, and model inference.
  - Integrate ML models and search capabilities into production systems.
  - Evaluate, fine-tune, and monitor search performance metrics.
  - Collaborate with software engineers, data engineers, and product teams to translate business needs into technical implementations.
  - Stay current with advancements in search technologies, LLMs, and semantic retrieval frameworks.

• Mandatory Skills Description:

  - 5+ years of experience in Data Science or Machine Learning Engineering, with a focus on Information Retrieval or Semantic Search.
  - Strong programming experience in both Java and Python (production-level code, not just prototyping).
  - Deep knowledge of Lucene, Apache Solr, or Elasticsearch (indexing, query tuning, analyzers, scoring models).
  - Experience with Vector Databases, Embeddings, and Semantic Search techniques.
  - Strong understanding of NLP techniques (tokenization, embeddings, transformers, etc.).
  - Experience deploying and maintaining ML/search systems in production.
  - Solid understanding of software engineering best practices (CI/CD, testing, version control, code review).

• Nice-to-Have Skills Description:

  - Experience of work in distributed teams, with US customers
  - Experience with LLMs, RAG pipelines, and vector retrieval frameworks.
  - Knowledge of Spring Boot, FastAPI, or similar backend frameworks.
  - Familiarity with Kubernetes, Docker, and cloud platforms (AWS/Azure/GCP).
  - Experience with MLOps and model monitoring tools.
  - Contributions to open-source search or ML projects.