Data Scientist (AI Platform Applications)
Job Description
We are looking for 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.