Sr. Data Scientist

ActioNet has an exciting opportunity for a Senior Data Scientist to join our team to lead digital transformation projects to develop and implement future mission-critical enterprise data analytics and production systems for the nation’s premiere source of statistical data products.

The Senior Data Scientist will provide full lifecycle data management including the design of highly scalable cloud-native architectures to support automated dynamic data collection, integration, storage, transformation, harmonization, analysis, reporting and dissemination needs.

This includes providing advanced subject matter expertise in AI/ML, data modeling, statistics, and data privacy protection.

This role demands expertise in Python and involves building complex pipelines from scratch, managing data flow within the AWS ecosystem, and conducting rigorous testing to ensure accuracy and completeness, consistency, and quality of all outputs.

This position requires a leader, innovator and problem-solver with solid data science, statistical, and programming experience.

This position also requires experience developing and designing new projects and solutions, coordinating projects between multiple teams with many stakeholders in a pro-active, enthusiastic communication style.

This position is REMOTE.

Federal position requires Public Trust.

Candidates must be US Citizens to be eligible.

• Serve as the technical authority for enterprise and solution-level data architecture, guiding the design and modernization of complex application ecosystems.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver tailored data solutions.
• Assess legacy and on-premises data environments and define cloud migration and modernization strategies including rehost, re-platform, refactor, and cloud-native redesign
• Define target-state data architectures that support front-end applications, API platforms, enterprise databases, queueing systems, scalable computation engines, and analytical services.
• Develop efficient data processing and transformation workflows to support analytics and reporting needs. Define architectures for computation engines that execute Python-based analytical workloads and large-scale data processing.