Data Scientist Associate Senior

Job responsibilities
 

• Designs, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services.
• Drives research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries.
• Collaborates with cross-functional teams to gather requirements and develop solutions that address organizational business needs.
• Communicates complex technical concepts and results effectively to both technical and non-technical stakeholders.
• Builds and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency.
• Develops and maintain tools and frameworks for prompt-based model training, evaluation, and optimization.
• Analyzes and interpret data to assess model performance and identify opportunities for improvement.

Required qualifications, capabilities, and skills
 

• Formal training or certification on data science concepts and 3+ years applied experience
• Proven experience in prompt design and implementation, or chatbot application development.
• Strong programming skills in Python, with expertise in PyTorch or TensorFlow.
• Experience building data pipelines for both structured and unstructured data. Proficiency in developing APIs and integrating NLP or LLM models into software applications.
• Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
• Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership.
• Working knowledge of deployment processes, including experience with GIT and version control systems.
• Familiarity with LLM orchestration and agentic AI libraries.
• Practical experience with MLOps tools and practices to ensure seamless integration of machine learning models into production environments.

Preferred qualifications, capabilities, and skills
 

• Familiarity with model fine-tuning techniques such as DPO (Direct Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback).
  • Knowledge of Java and Spark.