About Hypernative:

Hypernative is The leading SaaS security company focused on delivering real-time risk mitigation and security solutions to Web3 and blockchain ecosystems. Our platform provides cutting-edge capabilities to secure digital assets and ensure seamless, reliable operations for businesses operating in decentralized environments.

Hypernative is looking for a motivated and experienced Senior ML applied researcher to lead various efforts in the exciting world of smart contracts and web3/blockchains. The ideal candidate will have experience applying ML solutions on different problems, with emphasis on classical methods and analysis of security/fraud/AML/tabular data in a real time inference setting. The job requires identifying where a data driven solution can impact KPIs and being able to deliver and own a working solution to a production environment after the initial research stage.
Required skills:

• MSc in Statistics, CS, EE, Physics or similar with emphasis on a data related thesis. Alternatively 6+ years of experience in doing ML for fraud/AML/cyber security related applications
  
  
• Proven ability to take on a problem and come up with production ready solution while owning the entire life cycle (initial research and quick solutions, basic data engineering, deployments, monitoring, iterative improvements, continuous KPI measruement)
  
  
• Programming:
  • Good python programming skills, experience with working with cloud providers, preferably AWS
  • Experience in delivering production grade code in python which includes real time ML model inference (of any kind – NN, gradient boosted trees, LR…) and SQL usage
  • Good knowledge of SQL for research purposes
  • Familiarity with git and unit tests
    
    
• Experience in applying data driven solutions in security/fraud/AML - advantage
  
  
• Can do attitude, independent, team player, good prioritization skills
• Knowledge/interest in web3/blockchain - an advantage

All web3 related material will be thought on the job, there is no need for past knowledge in web3 (although nice)