Data Scientist III

Description and Requirements

• Design and develop Machine Learning and Generative AI solutions for complex business problems, applying statistical analysis, data mining, and machine learning techniques.
• Apply prompt engineering, RAG, and LLM evaluation in end-to-end AI application development.
• Work on complex problems where analysis of situations or data requires an in-depth evaluation of several factors.
• Collaborate with domain experts to understand business requirements and formulate data driven solutions.
• Deliver solutions to enterprise grade quality standards: rigorous, validated, reproducible, and ready for mission critical use.
• Define and implement evaluation frameworks for agent accuracy, task completion, reliability, and user satisfaction.
• Develop benchmarks, test datasets, and performance metrics for agentic AI systems.
• Contribute to AI governance, model validation, and continuous improvement initiatives.
• Implement strategies for model optimization, considering the constraints and requirements of mainframe environments.
• Support packaging and deployment of models and AI solutions into production environments.
• Explain complex technical concepts and predictive analytics clearly to both technical and non-technical audiences.
• Contribute to code reviews, technical documentation, and best practices.
• 6+ years of Data Science experience, with proven expertise in Machine Learning and Generative AI.
• B.Sc. in Computer Science, Engineering, or a related field.
• Experience working with Large Language Models (OpenAI, Anthropic, Gemini, Llama, or equivalent), with familiarity across different LLM architectures.
• Hands on experience with prompt engineering, Retrieval Augmented Generation (RAG), LLM evaluation, and AI application development.
• Experience designing performance metrics and evaluation frameworks for AI/ML systems.
• Familiarity with agentic frameworks and agent-based workflows using LangGraph, LangChain, or CrewAI.
• Strong proficiency in writing Python backend APIs (using FastAPI, Flask or similar) and in data science libraries such as Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow.
• Strong knowledge of statistical analysis, hypothesis testing, data mining, and machine learning techniques.
• Experience querying and analyzing data using SQL and modern data platforms.
• Proficiency in data exploration and data visualization techniques and tools.
• Familiarity with cloud platforms such as OpenShift (OCP) and AWS.
• Self-starter with intellectual curiosity; self-learner, flexible, and able to work in a multi tasked and dynamic environment.
• Excellent communication skills, with demonstrated ability to explain complex technical concepts and predictive analytics to non-technical audiences.
• Experience with LLM evaluation and observability tooling such as Langfuse, OpenTelemetry based stacks.
• Experience with vector databases and embedding models for retrieval and semantic search.
• Experience with MLOps practices such as experiment tracking, model registries, and pipelines (MLflow or Kubeflow).
• Experience with fine tuning and open weight models such as Llama, Mistral, and Qwen.
• Experience with Agile methodology and Atlassian products (Jira, Confluence).

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