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Mid-level, popular ML/NLP role in metro with broad skill requirements, increasing applicant competition.
Highly technical ML/NLP skills are transferable across industries but require specialized expertise.
Explicit years plus many mandatory ML, LLM, and infra skills imply strict filtering.
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Lead data science projects involving predictive modeling, natural language processing, statistical analysis, and machine learning to extract insights from large user datasets.
Collaborate with development and product teams to research, develop models, and create data products that enhance user experience and product offerings.
Develop scalable data systems with system architects, applying machine learning techniques to improve search and match functionalities, including LLM fine-tuning and vector search implementations.
4.5+ years of relevant experience as a Senior Software Engineer or equivalent in data science roles.
Proficient in Python with expertise in statistical and machine learning algorithms such as regression, decision trees, neural networks, deep learning, and ensemble methods.
Practical experience with LLM/RAG for search quality, including query understanding, semantic retrieval, reranker design, and frameworks optimizing prompt engineering (e.g., LangChain, CrewAI).
Experience with search technologies (Solr, OpenSearch, Elasticsearch), embedding & vector search, backend tech (Flask, Gunicorn), open source NLP libraries (Spacy, NLTK, Gensim), and deep learning libraries (TensorFlow, PyTorch).
Experienced data scientist with a strong background in both NLP and machine learning focused on search and recommendation systems.
Skilled in designing and optimizing classifiers and features with a hands-on approach to implementing scalable data products in collaboration with cross-functional teams.
Comfortable working on cutting-edge technology involving LLM fine-tuning, vector-based search systems, and integrating backend services in a product-driven environment.