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- Dartmouth, NS
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Data scientist with Java expertise
Salary
Job Description
Project Description: 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.Applying Instructions
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