Data Scientist (AI Platform Applications)

Job Description

We are looking for a contract Data Scientist to extend our in-house AI platforms into new business use cases, and to enhance these products based on evolving requirements.

This is a build-and-apply role, not a research role. You'll spend most of your time integrating existing capabilities into new stakeholder use cases and shipping improvements to the platforms themselves.

Key Responsibilities

• Apply existing in-house AI platform capabilities to new business use cases brought in by stakeholders across various business units — scoping the use case, mapping it to existing platform primitives, and building the integration.
• Enhance and extend current platform products: add new integrations, improve workflows, extend evaluation/monitoring coverage, and close gaps identified through production usage.
• Write production-grade code (Python required; Node.js/TypeScript a strong plus) for services, pipelines, and APIs that plug into the existing platform architecture.
• Work directly with data engineers and the platform team lead to understand system design constraints before extending the system.
• Collaborate with business stakeholders to translate requirements into a working feature — with a bias toward reusing existing platform components over building bespoke ones.
• Contribute to internal documentation and knowledge transfer so use cases you build can be maintained by the core team after handover.

Requirements

• Degree in computer science, data science, or related field. PhD not required — this role is evaluated on shipped work, not research output.
• 3+ years hands-on experience building production software, ideally including some LLM/GenAI application work (RAG, agents, tool-calling, prompt engineering).
• Strong software engineering fundamentals: clean Python, comfortable reading/extending an existing codebase, decent grasp of API design and cloud deployment (AWS preferred).
• Practical experience with at least one LLM framework or SDK (Google ADK, AWS AgentCore, LangChain, LangGraph, or direct API integration with GPT/Claude/Gemini) — depth of software engineering ability matters more than familiarity with a specific framework.
• Comfortable working within an existing platform's architecture and conventions rather than designing systems from scratch.
• Experience with time series analysis and traditional ML (forecasting, regression, classification, feature engineering) is a bonus, not a requirement.
• Able to work independently in a Kanban-style delivery environment with minimal ramp-up time.