Data Scientist

 <b>Description</b><br/>Blockaid is redefining trust in blockchain through advanced, integrated security. Our on-chain security platform is trusted by leading Web3 companies, including Coinbase, MetaMask, Uniswap, Backpack, Stellar, and others, to detect, understand, and automatically prevent or minimize the impact of fraud, scams, hacks, and financial threatsWe are backed by top-tier global investors, including Sequoia Capital, Ribbit Capital, and Cyberstarts—firms known for supporting the most innovative and influential companies in technology and cybersecurity<strong>About the role :</strong></p>Join a pioneering data science team that builds statistical and machine learning systems to solve new problems and improve existing ones. You’ll have end-to-end ownership of research and development of full data science pipelines, combining rule-based heuristics, machine learning models, and complex data pipelines into production-grade products that make a real impact on Web3 security</p><strong>Your Chain of Impact: </strong></p><ul><li>Analyze and interpret tabular, graph, and textual datasets to extract actionable insights.</li><li>Build complete data pipelines for feature engineering using SQL and Python.</li><li>Combine graph analysis algorithms with predictive modeling using tabular data.</li><li>Develop and deploy production pipelines that integrate results into the company’s core product.</li></ul><br/> <b>Requirements</b><br/><ul><li>4+ years of experience as a Data Scientist, preferably in the cybersecurity or Blockchain/Web3 space.</li><li>High proficiency in SQL and Python.</li><li>Bachelor’s or higher degree in a quantitative field (Computer Science, Applied Mathematics, Statistics, etc.)</li><li>Hands-on experience with machine learning frameworks and statistical modeling.</li><li>Proven experience deploying ML models into production environments.</li><li>Experience with data engineering for large-scale datasets.</li><li>Familiarity with tools like Snowflake, Airflow, and DBT - Advantage.</li></ul><br></p><br></p><br/>