Lead Data Scientist (Artificial Intelligence/Machine Learning)

WHAT IS INFORMATION TECHNOLOGY? A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions Position(s) are to be filled in the following area(s): IT - Taxpayer Services and Online Accounts Consider each location carefully when applying. If you are selected for a location, that location will become your official post of duty.

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The following are the duties of this position at the full working level. If this vacancy includes more than one grade and you are selected at a lower grade level, you will have the opportunity to learn to perform these duties and receive training to help you grow in this position. Serves as a Senior Data Scientist by leading advanced analytics projects, defining objectives, coordinating deliverables, evaluating team performance, and resolving challenges to ensure project success. Manages day-to-day team operations, and driven outcomes aligned with the organization's goals.Responsibilities include obtaining approvals on project documentation, conducting code and model reviews, and ensuring project delivery acceptance. Communicates findings effectively, providing insights and training on complex analytics solutions to business partners at all levels. Collaborates with senior leaders, technical teams, and non-technical staff to address policy interpretations and translate technical challenges into actionable solutions. Champions IT's digital transformation with a focus on customer-centric, data-driven initiatives, ensuring the analytics environment evolves to meet business needs. Leads efforts to certify datasets, optimize processes for data extraction, transformation, governance, and cataloguing, and accelerate time-to-insights for business decision-making. Participates in defining project objectives for advanced analytics, supervised and unsupervised machine learning (ML), and AI solutions. Collaborates with business units to gatherrequirements, establish metrics, and outline expected outcomes. Develops innovative approaches and methodologies, applying advanced operations research and data science techniques such as statistical analysis, forecasting, predictive modeling, prescriptive analysis, and optimization. Validates methodologies and outcomes to ensure accuracy and alignment with objectives. Identifies and implements methods, processes, algorithms, tools, and systems to extract insights from structured and unstructured data sets across the data science lifecycle. Responsible for developing algorithms and tools for data manipulation and processing and using data visualization techniques to clearly articulate findings for stakeholders.