Senior Data Scientist - Generative AI

We are looking for a Senior Data Scientist to design and deliver Generative AI applications as part of the Growth Analytics & AI team. You will focus on building innovative, scalable, and reliable LLM-based solutions that produce insights from conversational and long-form unstructured data to inform business strategy, increase operational efficiency, and improve customer experience.

The ideal candidate combines strong applied GenAI and machine learning expertise with solid software engineering practices, demonstrates curiosity in exploring new approaches and use cases, and communicates effectively with both technical and non-technical stakeholders. This role partners with product, engineering, and platform teams to translate complex business problems into production-ready AI solutions and move high-value use cases from concept to impact..

Key Responsibilities

• Design, build, and deploy LLM-based applications for conversational intelligence and insight generation.

• Develop multi-step AI workflows, including retrieval, orchestration, routing, and tool-using patterns.

• Define and implement evaluation approaches for LLM applications, including response quality, reliability, safety, and value.

• Work with large-scale structured and unstructured data using modern data platforms (e.g., Spark, Databricks, Snowflake).

• Partner with engineering teams to move GenAI solutions from prototype to production.

• Write clean, maintainable code and contribute to shared development practices such as version control, documentation, and code review.

• Ensure solutions align with enterprise standards for security, privacy, compliance, and responsible AI.

• Bachelor’s degree and 5+ years of experience, Master’s degree and 3+ years, or PhD candidate in computer science, data science, engineering, or a related quantitative field.

• Strong proficiency in Python and modern development practices for AI applications.

• Expertise in working with natural language data and building text-based products, using both classic and state-of-the-art NLP techniques (e.g. text mining, word embeddings, transformer-based models)

• Experience with LLM prompt engineering and architectural patterns for LLM systems (e.g., retrieval-augmented generation, multi-agent designs).

• Hands-on experience building tool-integrated and agent-based LLM workflows using frameworks such a