Senior Machine Learning Engineer

Role: Senior Machine Learning Engineer Location: St. Louis, Missouri Duration: 6+ Months ONLY H1B Candidates and Local candidates are preferred ( Ready to relocate to Client location ) Min 10+ Years Exp Required This is hybrid role in St. Louis, MO (2-3 days) The Digital and eCommerce team currently operates several B2B websites and direct digital sales channels via a globally deployed cloud-based platform that are a growth engine for Client life science business. We provide a comprehensive catalog of all products, enabling our customers to find products and purchase products as well as get detailed scientific information on those products. ESSENTIAL JOB FUNCTIONS • Machine Learning Model Development: Design, train, and evaluate ranking models (learning-to-rank, neural networks, embedding-based approaches) to optimize search relevance and personalization. • Search Query Analysis: Analyze search query logs, evaluate user behavior data to identify opportunities for relevance improvements and inform ranking strategies. • Feature Engineering: Develop and engineer features from search, product, and user data to power ML models and improve ranking performance. • Semantic Search & NLP: Implement semantic search for improved product discovery across chemistry and life science domains. • Search Engine Tuning: Optimize Elasticsearch/Lucene configurations, including tokenization, stemming, query parsing, and lexical search algorithms (BM25) to work in concert with ML models. • ML Pipeline Development: Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment using MLOps best practices. • Ranking & Personalization: Develop personalized ranking strategies that adapt to user segments, query intent, and business objectives; integrate collaborative filtering and content-based approaches. • Performance Monitoring & Iteration: Monitor search and ML model performance metrics in production; identify drift and continuously improve models based on new data and domain insights. • Search Relevance Documentation: Maintain clear documentation of search algorithms, ranking models, tuning strategies, and system configurations for internal teams. • Mentorship: Guide engineers on search relevance techniques, ML best practices, and data-driven problem-solving. QUALIFICATIONS Education: • Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field. Mandatory Skills: • 6+ years of hands-on experience in machine learning, data science, search relevance, or ranking systems. • Proven expertise in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn, or equivalent). • Strong background in statistical analysis, data exploration, and working with large-scale datasets. • Experience with feature engineering, data preprocessing, and data manipulation libraries (Pandas, NumPy, Spark). • Demonstrated experience building or working with ranking models (learning-to-rank, neural ranking, or similar). • Experience with semantic search, embeddings, or dense retrieval methods. • Deep understanding of search engines (Elasticsearch, Solr, OpenSearch), lexical search algorithms (BM25), information retrieval concepts, search relevance tuning, tokenization, stemming, and query parsing. • Experience with MLOps practices and tools (model versioning, experiment tracking, pipeline orchestration). • Proficiency in SQL and querying large datasets. • Strong problem-solving and analytical skills with the ability to think critically about complex search and ranking problems. • Excellent communication skills; ability to explain ML and search concepts to both technical and non-technical stakeholders. • Ability to collaborate with cross-functional teams