Staff Data Scientist

BitSight Technologies•Boston, MA

5d•$125,000 - $189,000

About The Position

Bitsight is a cyber risk management company whose solutions help organizations assess security performance, understand third-party exposure, and manage cyber risk. Methodology data scientists research, develop, and implement statistical and machine learning methodologies to assess cybersecurity risks. The Staff Data Scientist will help develop predictive cyber risk models, maintain and enhance Bitsight’s Security Ratings and Risk Vector methodologies, and translate analytical solutions into scalable production systems. The role collaborates with Engineering, Product Management, Marketing, Customer Success, and other teams to develop robust, explainable, data-driven solutions to cybersecurity problems. It calls for a strong background in statistics and machine learning, an interest in cybersecurity and risk assessment, and an interest in applying emerging AI technologies. The anticipated US hiring base salary range is $125,000–$189,000 annually. Pay within the range depends on work location and job-related factors such as skills, experience, and education or training. The role is also eligible for a bonus or commission plan and an equity grant.

Requirements

Master’s degree required; PhD preferred in Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or another quantitative science.
At least 1 year of equivalent industry experience at the staff level.
Proficiency with Git, Python or R, and SQL.
Experience with software development life cycle (SDLC) and package/library development.
Shell, Bash, or command-line scripting skills.
Experience with big data technologies such as Spark or Hadoop.
Knowledge of applied machine learning, predictive modeling, statistical analysis, and model validation.
Experience with data visualization and reporting tools.
AWS experience.
Ability to manipulate, clean, and combine large, complex, disparate data sources.
Ability to read, learn, and communicate algorithms and statistical methodologies from peer-reviewed literature.
Hands-on experience using AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot to improve coding, analysis, testing, and development productivity.

Nice To Haves

PhD in Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or another quantitative science.
Prior experience modeling risk for rare events.

Responsibilities

Research, develop, validate, and implement statistical and machine learning methodologies for cybersecurity risk assessment and prediction.
Maintain and enhance Security Ratings and Risk Vector scoring methodologies, ensuring accuracy, robustness, and explainability.
Explore cybersecurity, threat intelligence, and firmographic data sources to improve existing models and develop new capabilities.
Explore and apply emerging AI/ML techniques and tools to solve cybersecurity problems.
Develop and maintain scalable, reliable, well-documented, and tested code for data science models, scoring algorithms, and data processing pipelines.
Monitor model performance, investigate anomalies, and identify opportunities for improvement.
Communicate complex technical concepts and contribute to internal and customer-facing documentation.
Anticipate technical issues at the product level and make design decisions to avoid them.