Lead Data Scientist; Artificial Intelligence​/Machine Learning

Summary

Senior Data Scientist – IT – Taxpayer Services and Online Accounts. Responsibilities include leading advanced analytics projects, defining objectives, coordinating deliverables, evaluating team performance, and resolving challenges to ensure project success.

• Lead advanced analytics projects, define objectives, coordinate deliverables, and evaluate team performance.
• Manage day‑to‑day team operations and drive outcomes aligned with organizational goals.
• Obtain approvals on project documentation, conduct code and model reviews, and ensure project delivery acceptance.
• Communicate findings effectively, providing insights and training on complex analytics solutions to business partners at all levels.
• Collaborate with senior leaders, technical teams, and non‑technical staff to address policy interpretations and translate technical challenges into actionable solutions.
• Champion IT’s digital transformation with a focus on customer‑centric, data‑driven initiatives.
• Lead efforts to certify datasets, optimize processes for data extraction, transformation, governance, and cataloguing; accelerate time‑to‑insights for business decision‑making.
• Define project objectives for supervised and unsupervised machine learning (ML) and AI solutions; collaborate with business units to gather requirements, establish metrics, and outline expected outcomes.
• Develop innovative approaches and methodologies, applying advanced operations research and data science techniques such as statistical analysis, forecasting, predictive modeling, prescriptive analysis, and optimization.
• Validate methodologies and outcomes to ensure accuracy and alignment with objectives.
• Identify and implement methods, processes, algorithms, tools, and systems to extract insights from structured and unstructured datasets across the data science lifecycle.
• Develop algorithms and tools for data manipulation and processing; use data visualization techniques to clearly articulate findings for stakeholders.

• Basic requirement:
  Bachelor’s or higher degree in mathematics, statistics, computer science, data science, or a directly related field.
• OR a combination of 30 semester hours of coursework in a related major plus relevant experience.
• Specialized experience at GS‑13 level (mandatory):
  • Design, develop, integrate, test, and support conversational AI solutions, virtual assistants, chatbots, digital messaging platforms, voice automation, or generative AI‑enabled customer engagement solutions in a production environment.
  • Develop and optimize natural language understanding (NLU), natural language processing (NLP), speech recognition, intent classification, entity recognition, conversational workflows, or automated self‑service solutions supporting customer interactions across voice and digital channels.
  • Design, test, implement, and refine prompt engineering strategies, generative AI workflows, large language model (LLM) integrations, and AI‑assisted customer engagement capabilities.
  • Integrate conversational AI, generative AI, voice, chat, messaging, or digital engagement platforms with enterprise applications, APIs, backend systems, authentication services, customer data platforms, or knowledge management solutions.
  • Demonstrate SME‑level proficiency in Java or Python, including development of backend services, automation, integrations, data processing pipelines, or conversational application logic.
  • Analyze customer interaction data, conversation transcripts, chat sessions, operational metrics, and user behavior to identify trends and improve AI performance.
  • Develop, query, and analyze large datasets using cloud‑based analytics platforms and data warehouses to support AI model evaluation, operational reporting, and business decision‑making.
  • Troubleshoot and resolve complex system integration, application reliability, authentication, speech processing, conversational AI, generative AI, digital engagement, or performance issues across interconnected platforms.
  • Apply Dev Sec Ops , CI/CD pipelines, automated testing, version control, and agile software development practices in enterprise environments.
  • Collaborate with business stakeholders,…