We consistently top the charts as one of if not the most used Sports Betting website in the countries we operate in.
With millions of weekly active users, we strive to be the best in industry for our users.
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. Working closely with our Trading, Product, and Tech teams, you will leverage your expertise to tackle a wide range of business challenges, translating them into supervised and/or unsupervised learning problems.

WHO WE ARE

Sporty Group is a consumer internet and technology business with an unrivalled sports media, gaming, social and fintech platform which serves millions of daily active users across the globe via technology and operations hubs across more than 10 countries and 3 continents.
The recipe for our success is to discover intelligent and energetic people, who are passionate about our products and serving our users, and attract and retain them with a dynamic and flexible work life which empowers them to create value and rewards them generously based upon their contribution.
We have already built a capable and proven team of 300+ high achievers from a diverse set of backgrounds and we are looking for more talented individuals to drive further growth and contribute to the innovation, creativity and hard work that currently serves our users further via their grit and innovation.

REQUIREMENTS

2+ years in data science, ML, or software dev, including 6+ months hands-on with GPT-style models.
Proficient in core Python and key AI libraries (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 ROI.
Familiarity with vector databases (Pinecone, FAISS, Milvus) and API integration patterns.
Strong communication and collaboration abilities in fast-moving, cross-functional teams.

Own the full LLM lifecycle—prompt design, data prep, fine-tuning, deployment, and continuous optimization.
Build automated and human-in-the-loop evaluation pipelines to track quality, safety, and business 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.