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Lead end-to-end data science projects from business problem to production deployment
Develop ML and NLP models for classification, information extraction and retrieval
Build Gen AI agents, workflows, MCP servers and RAG architectures
Evaluate models through error analysis and performance monitoring
Work with Big Data infrastructure such as Elastic, Kafka, Spark, Redis and vLLM
Lead end-to-end projects: defining the business problem, scoping, setting success metrics (KPIs), model development, and production deployment
Develop and implement Machine Learning and NLP models: processing text from diverse sources for tasks such as classification, information extraction, and retrieval
Build advanced Gen AI environments: designing and building intelligent agents/workflows, developing MCP servers, context management, and implementing RAG architectures
Analysis and evaluation (Evaluation & Observability): extracting business insights, performing in-depth error analysis, and monitoring model performance
Work with Big Data infrastructure: deep understanding and development in environments including Elastic, Kafka, Spark, Redis, and vLLM
We are looking for an experienced Data Scientist for our Technology Division, which combines technological innovation with real purpose, working to build a better and more advanced AI system for millions of members. The division encourages broad use of AI tools to boost productivity and streamline processes, so candidates with hands-on AI tool experience will be given priority. An excellent opportunity to join the AI & Big Data team in one of the most fascinating and impactful domains. The role involves developing and implementing Data Science solutions in the domain, working with both tabular and textual data , combining classic ML models with advanced Generative AI technologies , and collaborating closely with a wide range of business and technical stakeholders across the organization. This opportunity is with Naya, an EPAM company, a leading global provider of data platforms and development professional services. Based in Israel, Naya is one of the fastest-growing companies in the data and development technology space, and we’re growing our team. Responsibilities Lead end-to-end projects: defining the business problem, scoping, setting success metrics (KPIs), model development, and production deployment Develop and implement Machine Learning and NLP models: processing text from diverse sources for tasks such as classification, information extraction, and retrieval Build advanced Gen AI environments: designing and building intelligent agents/workflows, developing MCP servers, context management, and implementing RAG architectures Analysis and evaluation (Evaluation & Observability): extracting business insights, performing in-depth error analysis, and monitoring model performance Work with Big Data infrastructure: deep understanding and development in environments including Elastic, Kafka, Spark, Redis, and vLLM Close collaboration with Data Engineers, analysts, and domain experts to create solutions with real business value Requirements At least 3 years of experience developing DS/ML models, with strong practical understanding of Classic ML — required Significant NLP experience, including working with open-source models and understanding of Transformers, BERT, LoRA, and SFT — required At least 1 year of significant, proven hands-on experience building Gen AI-based agents, tool-calling frameworks, and prompt optimization techniques — required At least 3 years of high-level experience developing in Python and SQL — required Experience deploying code to production, including CI/CD processes, Git, and dev/QA/production environments (e.g., Docker) — required Experience with infrastructure such as Spark / Elasticsearch / Kafka / Redis / vLLM — advantage Relevant Master's degree — advantage Ability to learn independently Strong interpersonal communication and presentation skills across various forums Critical thinking Analytical thinking and problem-solving ability
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Day 2
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From 9,971 similar postings that came down. Coming down is not always a hire.
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