Senior Data Scientist Consultant, Managed Agent Platform (MAP) – Agent Platform Team P2392 

Summary 

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the MAP (Managed Agent Platform) squad and is focused on helping stand up a new, centralized platform that simplifies and accelerates the design, development, deployment, and monitoring of AI agents across the organization. 

The MAP is being built in response to growing demand for a flexible, no-code/low-code solution that enables both technical and non-technical users to build and deploy agents without deep engineering overhead. The platform is early-stage — the team is actively evaluating different platforms and tooling, defining workflows, and establishing standards. You will be joining at the ground floor and helping shape what this platform becomes. 

You will be the bridge between the engineers building/maintaining the MAP and the data scientists and agent builders who will use it. You are not building agents for end-clients. Instead, you ensure the platform works for the people on it — that capabilities are tested, documented, and adopted smoothly, and that practitioner needs make it back to the engineering team in a way that shapes the roadmap. 

You’ll bring hands-on familiarity with the open-source agent ecosystem — including frameworks like LangChain, LangGraph, Google ADK, and OpenAI Agents SDK — and apply that knowledge to evaluate platform capabilities, inform design decisions, and help practitioners understand what’s possible. As the platform matures, you’ll help onboard users, develop best practices, and drive adoption across the organization. 

This is a small, engineer-heavy squad that is standing something up from scratch. Everyone pitches in on a bit of everything. Priorities will shift frequently as the platform takes shape. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority. 

Responsibilities 

• Voice of the practitioner: Serve as the primary liaison between data scientists and agent builders and the MAP engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions, standards, and the roadmap. 

• Platform evaluation & definition: Help evaluate and select platform tooling and contribute to defining core platform capabilities — including agent building workflows, orchestration patterns, tool/API connectivity, observability and tracing, evaluation frameworks, and governance controls. Test and document findings, recommended use cases, limitations, and rollout considerations. 

• Onboarding & enablement: As the platform matures, write guides, maintain documentation, lead training sessions, and run office hours to help data scientists and agent builders adopt the MAP. Serve as a first line of support when teams hit roadblocks. 

• Adoption & communication: Evangelize the MAP to increase awareness and drive adoption across the organization. Produce clear communications and materials for both technical and non-technical audiences. 

• Cross-functional collaboration: Work closely with engineers, the solution architect, and product lead on the squad to ensure platform capabilities have well-defined use cases and that guidelines and best practices are established. Act as an agent-building ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices. 

• Governance & compliance support: Help validate that platform capabilities meet enterprise requirements around security, compliance, observability, scalability, and data governance. 

Qualifications & Experience 

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline. 

• 2–4 years of experience in data science, AI/ML development, or applied analytics. 

• 2–4 years of experience in tech consulting, retail analytics, or related professional services. 

• Hands-on experience with Python and SQL. 

• Hands-on experience with one or more agent frameworks (e.g., LangChain, LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, AutoGen) is required. 

• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams. 

• Experience partnering with software engineering and/or product management teams. 

• Familiarity with LLMs, prompt engineering, and agentic AI concepts. 

• Exposure to agent platforms is a strong plus. 

Skills 

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation. 

• Ability to influence across teams and seniority levels without direct authority. 

• Comfort operating in ambiguity — this platform is early-stage, and priorities will shift frequently as the team learns what works. 

• Strong project and time management skills; able to manage multiple priorities with minimal oversight. 

• Deep curiosity about the agentic AI ecosystem and a genuine desire to become a subject matter expert on agent orchestration, evaluation, observability, and deployment patterns. 

• Comfort working on a small, engineer-heavy team where you are often the only non-engineer voice in the room. 

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