*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

*Education:*
* Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.

*Mandatory Skills:*
* 3 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 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.
* 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