Data Scientist I Agentic Services Pod Member

Our Client is looking for a Data Scientist I Agentic Services Pod Member to help business teams understand, adopt, and maximize the value of Generative AI (GenAI) tools and agentic capabilities. This role focuses on identifying business opportunities, translating business requirements into practical AI use cases, supporting experimentation and adoption, and connecting users with approved enterprise AI tools, platforms, and specialist resources.

The ideal candidate should have foundational knowledge of AI and Generative AI, along with strong communication, analytical, problem-solving, and collaboration skills. The individual will work closely with business stakeholders, AI product teams, data science teams, architecture, platform teams, and governance partners to support responsible AI adoption in alignment with enterprise standards, security requirements, and Responsible AI principles.

• Collaborate with business stakeholders to identify business problems, user requirements, process improvement opportunities, and potential applications of Generative AI and agentic capabilities.
• Work with the Data Science Pod Lead to align business engagement and AI enablement activities with team priorities, governance requirements, and expected outcomes.
• Partner with AI Product Owners and business teams to define use cases, business objectives, adoption challenges, success metrics, and expected value.
• Evaluate AI opportunities based on business needs, technical feasibility, data availability, risks, governance, adoption, and support requirements.
• Recommend appropriate solutions, including enterprise AI tools, configurable agents, reusable AI patterns, workflow improvements, and specialist delivery support.
• Configure, test, demonstrate, troubleshoot, and document AI prompts, assistants, agents, workflows, and related user experiences.
• Apply prompt engineering, system instructions, knowledge grounding, structured outputs, agent configuration, and AI evaluation techniques.
• Explain GenAI concepts, capabilities, limitations, risks, and suitable use cases to technical and non-technical audiences.
• Guide users on responsible AI usage, human oversight, approved enterprise tools, usage guidelines, and learning resources.
• Investigate issues related to prompt behavior, AI-generated outputs, access, configurations, knowledge grounding, compatibility, workflows, and integrations.
• Use diagnostics, logs, feedback, test cases, documentation, and platform guidance to troubleshoot issues and coordinate with specialist teams when required.
• Document issues, resolutions, workarounds, lessons learned, and reusable guidance to improve AI adoption.
• Collaborate with architecture, platform, data, engineering, cybersecurity, legal, privacy, Responsible AI, learning, and change-management teams.
• Support experimentation, validation, adoption, and continuous improvement of AI-enabled business solutions.

• Bachelor's degree in a relevant technical or quantitative field.
• Minimum of 2 years of relevant professional experience. Internships do not count toward the required experience.
• Working knowledge of Artificial Intelligence, Generative AI, agentic concepts, and business productivity technologies.
• Familiarity with configuring, testing, demonstrating, troubleshooting, and documenting AI prompts, assistants, agents, or workflows.
• Understanding of prompt engineering, system instructions, knowledge grounding, agent configuration, and structured outputs.
• Digital literacy across GenAI tools, collaboration platforms, workflow tools, data sources, APIs, and basic scripting or analytical tools.
• Strong verbal and written communication skills.
• Excellent problem-solving abilities, initiative, patience, and willingness to learn.
• Ability to collaborate effectively with technical teams, business stakeholders, and end users.

• Degree in Data Science, Applied Statistics, Business Analytics, Business Intelligence, Computer Science, Engineering, Mathematics, Informatics, Information Systems, Predictive Analytics, or a related technical field.
• Experience in statistical analysis, including regression, hypothesis testing, ANOVA, and statistical process control.
• Familiarity with machine learning techniques such as clustering, logistic regression, decision trees, Random Forests, Support Vector Machines, and neural networks.
• Two or more years of Python programming experience using libraries such as NumPy, SciPy, and pandas to solve business problems.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience in industrial sectors such as heavy equipment, automotive, aerospace, engines, transmissions, construction, or mining.
• Understanding of the AI solution lifecycle, including discovery, use-case definition, experimentation, validation, approval, adoption, deployment, support, benefits measurement, and retirement.
• Familiarity with AI governance, risk reviews, documentation, testing, change management, release processes, and benefits tracking.
• Commitment to continuous learning and applying emerging technologies to solve business challenges.

1. Generative AI Tools and Agent Configuration

• Ability to configure, test, demonstrate, troubleshoot, and document GenAI tools, assistants, agents, prompts, and workflows.
• Understanding of practical applications of AI assistants and agentic solutions in business processes.

2. Prompt Engineering and AI Evaluation

• Knowledge of prompt design, system instructions, knowledge grounding, structured outputs, and agent configuration.
• Ability to evaluate AI-generated responses, troubleshoot prompt behavior, and guide users on effective AI usage.

3. Digital and Analytical Literacy

• Familiarity with collaboration platforms, workflow tools, data sources, APIs, and basic scripting or analytical tools.
• Strong analytical thinking, with deeper technical and programming skills considered an advantage.

• Excellent verbal and written communication.
• Strong teamwork and collaboration.
• Patient, supportive, and user-focused approach.
• Self-motivation and initiative.
• Strong analytical and problem-solving abilities.
• Ability to explain technical concepts in simple, business-friendly language.
• Focus on enterprise-wide value, responsible AI adoption, and measurable business outcomes.