About the job
About SecurityScorecard:
SecurityScorecard stands at the forefront of cybersecurity ratings, continuously assessing over 12 million organizations across 64 countries. Established in 2013 by security and risk visionaries Dr. Alex Yampolskiy and Sam Kassoumeh, and backed by distinguished investors, our patented technology empowers more than 25,000 organizations worldwide in self-monitoring, managing third-party risks, board reporting, and cyber insurance underwriting. This commitment enhances organizational resilience, making it easier to identify and rectify cybersecurity vulnerabilities across their digital landscape.
Located in New York City, our workplace culture has garnered recognition from Inc Magazine as a "Best Workplace," and from Crain’s NY as a "Best Places to Work in NYC". Furthermore, we have been celebrated as one of the 10 hottest SaaS startups in New York for two consecutive years. Recently, SecurityScorecard was honored in Fast Company’s list of the World’s Most Innovative Companies for 2023, and received the Achievers 50 Most Engaged Workplaces award for our steadfast dedication to employee engagement. We are proud to be supported by leading investors, including Silver Lake Waterman, Moody’s, Sequoia Capital, Google Ventures, and Riverwood Capital.
Role Overview:
We are seeking an accomplished and motivated Senior Data Scientist to join our dynamic Data Science team, leveraging our extensive and unique cybersecurity data assets. In this pivotal role, you will spearhead the development and implementation of advanced machine learning models that yield insights and enhance our cybersecurity platform. You will tackle complex challenges, collaborate with cross-functional teams, and directly contribute to the success of our offerings.
Responsibilities:
- Design, evaluate, and deploy machine learning models for cybersecurity risk scoring, threat intelligence, and vendor risk assessment.
- Conduct exploratory data analysis to uncover patterns, trends, and anomalies within large and complex datasets.
- Assess and validate models, ensuring their accuracy, robustness, and scalability.
- Work closely with product managers and engineers to define data science requirements and seamlessly integrate models into production systems.
- Engage in code reviews, design discussions, and promote data science best practices.
- Stay abreast of the latest research and technologies to leverage cutting-edge methodologies.
- Effectively communicate findings to stakeholders and provide actionable recommendations.

