ML Engineer I – Data Scientist (eCommerce Search)

ML Engineer I – Data Scientist (eCommerce Search)

St. Louis, MI, USA (Onsite – 5 Days)

Contract

UST Global // Sigma-Aldrich

Job Summary

We are seeking an ML Engineer I / Data Scientist specializing in eCommerce Search to build intelligent search experiences for a global digital commerce platform. The ideal candidate will have hands-on experience in machine learning, search relevance, semantic search, ranking algorithms, and MLOps. You will work with cross-functional teams to improve product discovery, search performance, and personalized customer experiences through advanced AI/ML solutions.

Key Responsibilities

• Design, develop, and evaluate machine learning models for search relevance and ranking.
• Build learning-to-rank, neural ranking, and embedding-based search models.
• Analyze search queries and user behavior to improve search quality.
• Engineer features from search, product, and customer data.
• Implement semantic search and NLP techniques for product discovery.
• Tune Elasticsearch/OpenSearch/Lucene configurations including BM25, tokenization, stemming, and query parsing.
• Develop end-to-end ML pipelines covering preprocessing, training, evaluation, deployment, and monitoring.
• Build personalized ranking and recommendation strategies.
• Monitor production model performance and continuously improve relevance.
• Collaborate with Product Owners, Data Scientists, and Engineering teams to deliver scalable AI-powered search solutions.

Required Skills

• 3+ years of experience in Machine Learning, Data Science, Search Relevance, or Ranking Systems.
• Strong Python programming skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or MLflow.
• Strong understanding of statistics and data analysis.
• Experience with Pandas, NumPy, Apache Spark, and large-scale data processing.
• Hands-on experience with Learning-to-Rank or Neural Ranking models.
• Experience with Semantic Search, Embeddings, or Dense Retrieval.
• Expertise in Elasticsearch, Solr, or OpenSearch.
• Knowledge of BM25, Information Retrieval, Query Parsing, Tokenization, and Stemming.
• Experience implementing MLOps practices and model lifecycle management.
• Strong SQL skills.
• Excellent analytical, problem-solving, and communication skills.

Preferred Skills

• Model training and fine-tuning.
• Experience with Large Language Models (LLMs).
• Prompt Engineering.
• Vector Search / Dense Vector Databases.
• Search metrics analysis and optimization.
• Tableau or Looker.
• NLP or Information Retrieval background.
• Experience working on Search or ML-focused product teams.

Nice to Have

• Experience with eCommerce Search platforms.
• Knowledge of Microservices architecture.
• Event-driven systems.
• CI/CD pipelines.

Education

• Bachelor''s degree in Computer Science, Data Science, Engineering, or a related quantitative discipline.

Additional Technical Stack

• Python
• MLflow
• TensorFlow
• PyTorch
• Scikit-learn
• Pandas
• NumPy
• Apache Spark
• SQL
• Elasticsearch
• OpenSearch
• Solr
• BM25
• Semantic Search
• NLP
• Learning-to-Rank
• Embeddings
• MLOps
• Tableau
• LLMs
• CI/CD