Senior Data Scientist

Responsibilities:

• • Drive the development and evolution of customer clustering models using unsupervised learning to identify patterns in pass rate performance and flag inconsistencies. 
  • Lead SHAP-based explainability initiatives to uncover the root causes behind verification failures and create dynamic, on-demand explanations. 
  • Conduct benchmarking of LLM APIs, assessing summarization quality, latency, relevance, and cost to inform GenAI solution design. 
  • Collaborate on pipeline development to extract, preprocess, and format QuickSight reports for GenAI consumption. 
  • Build and test proof-of-concept RAG pipelines that enhance LLMs with domain-specific context from historical documents and verification reports. 
  • Work closely with Delivery Managers to translate complex analytics and model outputs into business-friendly visualizations and narratives. 
  • Continuously refine clustering methodology by evaluating alternative models, tuning hyperparameters, and expanding criteria.  
  • Partner with MLOps engineers to ensure seamless integration of data science pipelines into the broader infrastructure, with a focus on automation and scalability. 

• 4+ years of experience as a Data Scientist, with deep expertise in unsupervised learning, clustering, and advanced exploratory data analysis. 
• Strong hands-on experience with SHAP or similar model interpretability techniques. 
• Proficiency in Python, Pandas, SQL, Jupyter, and common data manipulation and visualization tools. 
• Familiarity with AWS ecosystem tools like S3, RDS, IAM, and BI solutions such as QuickSight. 
• Experience designing and building GenAI or LLM-based workflows, including prompt engineering and integrating APIs. 
• Ability to benchmark different LLM solutions and assess their performance for specific summarization and recommendation use cases. 
• Skilled in transforming raw outputs into compelling, business-relevant insights for both technical and non-technical audiences. 

Nice to have 

• Experience implementing RAG pipelines with vector databases and domain document ingestion. 
• Exposure to MLOps workflows and tooling (e.g. MLflow,  SageMaker, Airflow, Terraform). 
• Prior work on integrating BI platforms with AI/ML pipelines. 
• Background in identity verification