Relevance AI is a machine learning platform to enable developers to experiment and build important AI application with data. We do this by focusing on providing developer tooling around an important data type called vectors that can accelerate AI development significantly.
We are hiring a Data Scientist - Gaming to work with our data scientists and machine learning engineers to build a suite a gaming products to bring the power of AI to gamers. You will be joining a rapidly growing team backed by Insight Partners (investor in Monday.com, Twitter, etc) where new ideas and state of the art Machine Learning is applied daily.
As a Data Scientist, you will be responsible for creating statistically insightful analysis of popular games to help gamers improve.
This role is for someone who is a passionate gamer, decisive, moves incredibly fast and has a strong appetite for growth. You will be essential in delivering the vision of the team.
You will be joining a rapidly growing team backed by Insight Partners (investor in Monday.com, Twitter, etc) where new ideas and state of the art Machine Learning is applied daily.
Work with our gaming clients needs and work with them to illustrate and integrate Relevance AI to value for gamers.
Analyze and preprocess raw data: assessing quality, cleansing, structuring for ingestion into Relevance AI.
Collaborate with engineering team to bring your customer learnings to inform the direction of future product feature development.
Build and show demos and proof-of-concept using the platform.
Self starter, take ownership of their work and the quality of it.
Gamer! You love playing games such as teamfight tactics, league of legends, valorant, world of warcraft, etc.
Degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
At least 3 years of hands-on experience in using Python for Data Science with projects and outcomes to show for it.
Understanding of Vectors/Deep Learning embeddings and have experience in utilising them for search, recommendations, personalisation, etc.
Deep understanding of traditional statistical modeling: clustering, dimensionality reduction, K-nearest neighbors.
Experienced in using API frameworks such as Flask/Fastapi to create APIs in Python
At Relevance AI our mission is to accelerate developers to solve similarity and relevance problems through data, this enable important use cases such as recommendations, topic modelling, semantic search, zero-shot classification and more.
Our first step towards helping teams solve similarity and relevance, we started with the data type that all the top tech companies use - Vectors, a high dimensional representation of data used to determine similarities between data.