Role & Responsibilities

As an AI Data Application Specialist, you will be part of a growing AI Center of Excellence, working to integrate AI into data-driven decision-making across agencies. This role involves analyzing existing data architectures, developing AI-powered prototypes, and consulting on AI solutionsbridging the gap between technical execution and business impact.

The focus is on strategic system design and AI consulting, rather than day-to-day coding. You will work collaboratively to develop AI-powered workflows, optimize data pipelines, and explore generative AI applications to enhance business intelligence.

1. AI Strategy & Consulting for Data System Optimization:

- Advise on how AI can enhance data systems within different business and technology environments.

- Assess current data architectures and identify opportunities for AI integration.

- Design sustainable AI-driven data solutions, considering scalability, maintenance, and real-world business impact.

- Communicate the advantages and limitations of AI-powered data workflows to key stakeholders.

2. AI-Powered Data Pipeline Design & Prototyping:

- Develop AI-supported data systems, integrating RESTful APIs, relational databases, and cloud storage solutions.

- Use Python, Numpy, Pandas, Scikit-learn, and Scipy to analyze data and build AI-enabled solutions.

- Design and implement machine learning models to solve key business challenges.

- Collaborate with cross-functional teams, utilizing version control and code review practices.

3. Research & AI Innovation for Business Solutions:

- Stay informed on Generative AI, Large Language Models (LLMs), and multimodal AI developments.

- Explore new AI-driven business applications, providing recommendations on AI adoption strategies.

- Build AI prototypes to assess feasibility, efficiency, and long-term viability.

Ideal Candidate

- 2+ years of experience in data science, machine learning, or applied statistics.

- Strong consulting and communication skillsability to simplify complex AI concepts for non-technical audiences.

- Fluent in English (mandatory)comfortable engaging with global teams and stakeholders.

- Proficiency in Python and data analysis tools (Pandas, Numpy, Scikit-learn, etc.).

- Experience working with data pipelines, APIs, and cloud platforms (AWS, GCP, or Azure).

- Familiarity with Generative AI tools and LLMs (e.g. ChatGPT, Gemini, Claude) and their integration into data systems.