Data Scientist

A hands-on-first product role that grows into a leadership one, owning a data product from business problem to shipped feature, with the technical center of gravity on AI and ML engineering. You'll be close to the models that power our product: maintaining them, fine-tuning them per client, and running the LLM orchestration behind our text and image outputs. Data analysis is still a core skill. You'll start as an individual contributor and build a specialized team as the product grows.

The core responsibilities for the job include the following:

AI/ML ownership (core):

• Maintain and improve the different models running in our product, and keep them performing in production.
• Fine-tune and adapt models for individual clients' use cases, such as next-best-action and recommended products.
• Own LLM orchestration end-to-end, including generating and quality-checking both text and image outputs.
• Build, maintain, and improve RAG pipelines' retrieval quality, grounding, and evaluation.
• Define success metrics for each model, evaluate outputs, and make sure model-driven features hold up for real clients.

Product and delivery:

• Own the roadmap and outcomes for the data product, and prioritize what gets built.
• Translate business and client problems into clear, technical requirements the dev and AI/ML teams can build against.
• Work closely with engineering, data, and AI/ML teams to scope, sequence, and ship features for clients.

Data analysis (core):

• Analyze product, customer, and campaign data yourself to guide decisions and evidence model performance.
• Use your data-engineering understanding to design features that are realistic, well-modelled, and maintainable.

Client and stakeholder handling:

• Work directly with clients to gather requirements, validate direction, and turn feedback into product improvements.
• Manage stakeholder expectations, timelines, and communication.
• Team building starts hands-on as an individual contributor.
• As the product scales, hire and grow a team with the right specializations (data science, ML engineering, analytics, and PM), and lead it.

Requirements:

• 8-12 years in product management or a closely related technical role.
• Data analysis as a core, hands-on strength.
• Hands-on AI/ML experience maintaining and fine-tuning models in production, ideally across multiple client use cases.
• Experience with LLM orchestration and generating and evaluating text and image outputs. RAG experience building and improving retrieval pipelines.
• Strong data-engineering understanding enough to design features and speak the dev team's language.