Data Scientist (SPE)

• Lead end-to-end development and deployment of GenAI and machine learning models, including LLMs, transformer architectures, and deep learning solutions.
  
• Design and implement advanced recommendation systems — Search → Match, Search → Match → Rank, content-based, collaborative, and two-tower architectures.
  
• Build data-driven models that address critical business questions and drive strategic, measurable outcomes.
  
• Design and conduct rigorous data analyses with a focus on study design, methodology, algorithms, and statistical modeling.
  
• Build AI solutions aligned with the FATE framework to support fairness, accountability, transparency, and explainability.
  
• Translate complex data insights into strategies aligned with business goals.
  
• Use Python, SQL, and a modern data stack (Azure, Snowflake) to build scalable, efficient solutions.
  
• Use NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn to deliver production-grade ML models. Partner cross-functionally with engineering, product, and analytics teams; clearly communicate trade-offs,assumptions, and uncertainties to stakeholders.
  
• Establish and champion best practices for shipping ML models to production.
  

Requirements

• Master's degree in Computer Science, Computational Sciences, Data Science, Machine Learning, Statistics,
  Mathematics, or another quantitative field.
• Deep, hands-on expertise in ML/AI beyond tutorials or side projects.
  
• Proven production experience with LLMs, RAG, transformer architectures, deep learning, XGBoost, CatBoost,
  multivariate regression, classification, clustering, and topic modeling.
• Experience building recommendation systems, including Search → Match / Search → Match → Rank frameworks
  and content, collaborative, and two-tower architectures.
• Strong programming skills in Python and SQL; fluency in NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn.
  
• Proficiency with visualization tools such as Power BI.
  
• Practical knowledge of cloud platforms, particularly Azure and Snowflake.
  
• Demonstrated ability to ship ML models to production and manage deployment/maintenance.
  
• Experience working in both Agile and Waterfall environments.
  
• Self-directed, works effectively with minimal supervision; up to ~10% international travel may be required.
  

Benefits

• Competitive compensation and benefits.
  
• Opportunity to shape technology strategy and business outcomes.
  
• Strong learning and leadership growth opportunities.