Description

As a Data Scientist at Sporty, you will play a vital role in developing innovative data science solutions and machine learning models to drive business impact. You'll work closely with our Trading, Product, and Tech teams to tackle a wide range of business challenges, translating them into supervised and/or unsupervised learning problems.

Responsibilities

• Own the full Large Language Model (LLM) lifecycle—from prompt design and data preparation to fine-tuning, deployment, and continuous optimization.
• Build automated and human-in-the-loop evaluation pipelines to track quality, safety, and business Key Performance Indicators (KPIs).
• Integrate LLM services into products via REST/GraphQL APIs and vector-database retrieval.
• Partner with product and engineering to turn business problems into AI features that boost revenue and user engagement.
• Monitor models in production, manage versioning/rollback, and lead basic MLOps workflows.
• Evangelize best practices and mentor teammates on emerging LLM tools and responsible AI.

Requirements

• 2+ years of experience in data science, Machine Learning (ML), or software development, including at least 6 months of hands-on experience with GPT-style models.
• Proficient in core Python and key AI libraries (e.g., transformers, LangChain/LlamaIndex, FastAPI/Flask).
• Demonstrated skill in prompt engineering and parameter-efficient fine-tuning (e.g., LoRA, PEFT).
• Experience designing and running evaluation frameworks for relevance, safety, and Return on Investment (ROI).
• Familiarity with vector databases (Pinecone, FAISS, Milvus) and API integration patterns.
• Strong communication and collaboration abilities in fast-moving, cross-functional teams.

Nice to Have

• Exposure to MLOps tooling such as Docker, MLflow, GitHub Actions, or Hugging Face Hub.
• Knowledge of Retrieval-Augmented Generation (RAG) architectures and embedding techniques.
• Track record of shipping AI features in production SaaS, gaming, or fintech environments.
• Understanding of A/B testing and product analytics to measure model impact.
• Cloud experience on AWS, GCP, or Azure, especially with managed AI/DB services.
• Interest or background in quantitative methods or casino-gaming analytics.

Skills and competencies show up in different forms and can be based on different experiences, so we strongly encourage you to apply even if you don't have all the requirements listed above.