Data Sceintist (eCommecre Search Exp)

ONSITE ROLE Position Title: Data Scientist – eCommerce Search Exp Location: St. Louis, MO Duration: 12+ Months Contract Position Type- C2C/W2/1099 Exp Level- 10+Years Req Skills- Data Scientist, Digital, eCommerce Search, (machine learning, data science, search relevance, or ranking systems), Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit-learn, or equivalent), (Elasticsearch, Solr, OpenSearch), lexical search algorithms (BM25), SQL Role Description – • Our Digital and eCommerce division is looking to transform the Digital and eCommerce technology engine for Merck. • As a Data Scientist – eCommerce Search, you will play pivotal role in building the next generation of intelligent, high-performing search experiences for our global eCommerce platforms (e.g., sigmaaldrich.com and sigmaaldrich.cn) and build new features and components in our evolving platform, helping to embrace with search metrics, dashboards, model fine tuning. • You will be responsible for optimizing search relevance, tuning search engine behavior, and applying advanced AI/ML techniques to elevate how users discover and interact with products. • You’ll work closely with Product Owner, Data Scientists, and Software Engineers to deliver seamless and personalized search experiences that directly impact business outcomes. ABOUT OUR TECHNOLOGY • 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 Merck’s 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. • Data Analysis Mandatory Skills • 4 years of hands-on experience in machine learning, data science, search relevance, or ranking systems. • Proven expertise in Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit-learn, or equivalent). • Strong background in statistical analysis, data exploration, and working with large-scale datasets. • Experience in eCommerce Search • 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, embedding, 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.