What are the responsibilities and job description for the ML Engineer I – Data Scientist (eCommerce Search) position at Pacific Consultancy Services?
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