Senior Data Scientist

What you'll do

• Design and execute end-to-end machine learning pipelines
• Develop machine learning pipelines using Azure Synapse, Databricks, and Snowflake
• Build and deploy classification, regression, and clustering models
• Develop and deploy proof-of-concept solutions for client use cases
• Implement semantic matching and similarity search
• Build embedding models and optimise embedding storage
• Train and optimise models for new data providers
• Improve LDA model performance
• Implement hybrid semantic search
• Optimise RAG architectures and retrieval QA systems
• Enable semantic query understanding
• Develop forecasting models for marketing and demand prediction
• Apply NLP-based forecasting techniques
• Use semantic similarity for audience intelligence
• Migrate data pipelines and retrain models
• Optimise embedding storage and retrieval
• Perform vector index tuning and benchmarking

Requirements

• Design and execute end-to-end machine learning pipelines including data extraction, preprocessing, feature engineering, model development, tuning, and deployment
• Develop machine learning pipelines using Azure Synapse, Databricks, and Snowflake
• Build and deploy classification, regression, and clustering models
• Develop and deploy proof-of-concept solutions for client use cases
• Implement semantic matching and similarity search using cosine similarity, dot-product scoring, and bi-encoder/cross-encoder architectures (e.g., SBERT, sentence-transformers)
• Build embedding models by fine-tuning pre-trained models and optimising embedding storage in vector databases such as Chroma DB, FAISS, and Azure AI Search
• Train and optimise models for new data providers with dynamic input handling
• Improve LDA model performance for large-scale topic modelling
• Implement hybrid semantic search by combining dense and sparse retrieval methods
• Optimise RAG architectures and retrieval QA systems for chatbot and recommendation performance
• Enable semantic query understanding using intent classification and query expansion techniques
• Develop forecasting models for marketing, demand prediction, and trend analysis
• Apply NLP-based forecasting techniques using sentiment and external data
• Use semantic similarity for audience intelligence, including zero-shot and few-shot classification techniques
• Migrate data pipelines from Azure Synapse to Azure Databricks and retrain models accordingly
• Optimise embedding storage and retrieval within Azure AI Search
• Perform vector index tuning including HNSW optimisation and ANN benchmarking for production systems

Tech stack