Senior Data Scientist, Socure

• Education: ME/MSc in Computer Science, Industrial Engineering, Electrical Engineering, Operations Research, Mathematics, Statistics, or a related field, or equivalent work experience. PhD strongly preferred
• Experience:
• A minimum of 4 years of industry experience working in a similar role
• PhD + a minimum of 2 years of industry experience working in a similar role is strongly preferred
• Proficient in developing machine learning models and data-driven algorithms in information retrieval, relevance, or machine learning
• Extensive experience with Hadoop, Spark, and other distributed systems
• Familiarity with graph technology, including graph databases and graph-based algorithms, for analyzing and interpreting complex networks
• Strong programming skills in Python (especially pySpark) and a good working knowledge of SQL
• Experience working with AWS, Databricks, and other Big Data tools
• Excellent problem-solving and analytical skills

Desirable

• The Socure DS Fraud team is responsible for developing cutting edge Fraud & Risk products to detect identity theft, synthetic identities, first-party fraud, etc., building machine learning models using latest ML algorithms and technologies, building data-processing pipelines, evaluating the performance of new data sources, and providing analytical support to the Socure Fraud and Risk product suite. ​
• Develop machine learning, data mining, statistical, and graph-based algorithms designed to analyze massive data sets, uncovering key insights in fraud and risk patterns
• Collaborate closely with Cloud technologists to ensure algorithms are properly implemented and optimized for scale within cloud environments
• Analyze large data sets to develop multiple, custom models and algorithms to drive innovative correlations for fraud and acceptance data as well as social data
• Implement new data modeling solutions and improvements to existing solutions
• Integrate new data sources and API third-party products in the main framework
• Work with Big Data technologies using Spark
• Provide analytical support to the data science team
• Own and drive projects from conception to completion
• Collaborate with data scientists, engineers and other key stakeholders
• Work well in a fast-paced cross-functional environment

• End Date: March 31, 2025