Senior Data Scientist
- Role
- Data Science
- Experience
- Senior
- Company size
- Enterprise
Open to AR only. Set where you work from to check your eligibility.
No BS summary
Senior Data Scientist with 5+ years applying machine learning to real-world problems. Needs Python, ML/data science frameworks, cloud-based data pipelines, and model deployment experience. Remote role hiring in Argentina; cybersecurity, risk scoring, LLMs, or Scala are pluses.
Core skills
Required skills
Optional skills
About SecurityScorecard: SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard’s patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint. Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace,” by Crain’s NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company’s annual list of the World’s Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing “forward-thinking employers for their unwavering commitment to employee engagement.” SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital. About the Role: We are looking for a talented and driven Senior Data Scientist to join our growing Data Science team. As a Senior Data Scientist, you will play a crucial role in developing and implementing advanced analytical models that drive insights and enhance our cybersecurity ratings platform. You will work on challenging problems, collaborate with cross-functional teams, and contribute directly to the success of our products. Responsibilities: Build, evaluate, and deploy machine learning models for risk scoring, threat intelligence, and vendor risk assessment. Perform exploratory data analysis and identify patterns, trends, and anomalies in large and complex datasets. Evaluate and validate models, ensuring their accuracy, robustness, and scalability. Collaborate with product managers and engineers to define data science requirements and integrate models into production systems. Contribute to code reviews, design discussions, and data science best practices. Stay current on new research and technologies to ensure we leverage state-of-the-art methods. Communicate findings and recommendations effectively to both technical and non-technical audiences. Required Qualifications: Advanced degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or equivalent professional experience applying data science to complex problems. 5+ years of experience applying data science and machine learning to real-world challenges. Proficient in Python and ML/data science frameworks (e.g., scikit-learn, XGBoost, MLFlow, PyTorch). Experience working with cloud-based data pipelines (AWS, GCP, or similar). Solid understanding of ML algorithms, evaluation metrics, and model deployment. Effective communicator and team player with a passion for solving hard problems. Prior exposure to cybersecurity, threat intelligence, or risk scoring a significant plus. Prior exposure and interest in LLMs is a plus. Working knowledge of Scala is a plus. Benefits: Specific to each country, we offer a competitive salary, stock options, Health benefits, and unlimited PTO, parental leave, tuition reimbursements, and much more! The estimated total compensation range for this position is $80,000 USD - $100,000 USD (base plus bonus). Actual compensation for the position is based on a variety of factors, including, but not limited to affordability, skills, qualifications and experience, and may vary from the range. In addition to base salary, employees may also be eligible for annual performance-based incentive compensation awards and equity, among other company benefits. SecurityScorecard is committed to Equal Employment Opportunity and embraces diversity. We believe that our team is strengthened through hiring and retaining employees with diverse backgrounds, skill sets, ideas, and perspectives. We make hiring decisions based on merit and do not discriminate based on race, color, religion, national origin, sex or gender (including pregnancy) gender identity or expression (including transgender status), sexual orientation, age, marital, veteran, disability status or any other protected category in accordance with applicable law. We also consider qualified applicants regardless of criminal histories, in accordance with applicable law. We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact talentacquisitionoperations@securityscorecard.io. Any information you submit to SecurityScorecard as part of your application will be processed in accordance with the Company’s privacy policy and applicable law. SecurityScorecard does not accept unsolicited resumes from employment agencies. Please note that we do not provide immigration sponsorship for this position. #LI-DNI
What you'll do
- Build, evaluate, and deploy machine learning models for risk scoring, threat intelligence, and vendor risk assessment.
- Perform exploratory data analysis and identify patterns, trends, and anomalies in large and complex datasets.
- Evaluate and validate models, ensuring their accuracy, robustness, and scalability.
- Collaborate with product managers and engineers to define data science requirements and integrate models into production systems.
- Contribute to code reviews, design discussions, and data science best practices.
- Stay current on new research and technologies to ensure we leverage state-of-the-art methods.
- Communicate findings and recommendations effectively to both technical and non-technical audiences.
What they require
- Advanced degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or equivalent professional experience applying data science to complex problems.
- 5+ years of experience applying data science and machine learning to real-world challenges.
- Proficient in Python and ML/data science frameworks (e.g., scikit-learn, XGBoost, MLFlow, PyTorch).
- Experience working with cloud-based data pipelines (AWS, GCP, or similar).
- Solid understanding of ML algorithms, evaluation metrics, and model deployment.
- Effective communicator and team player with a passion for solving hard problems.
- Preferred: Prior exposure to cybersecurity, threat intelligence, or risk scoring a significant plus.
- Preferred: Prior exposure and interest in LLMs is a plus.
- Preferred: Working knowledge of Scala is a plus.
Benefits
- Competitive salary.
- Stock options.
- Health benefits.
- Unlimited PTO.
- Parental leave.
- Tuition reimbursements.
- Employees may also be eligible for annual performance-based incentive compensation awards and equity.
SecurityScorecard is a global leader in cybersecurity ratings, with over 12 million companies continuously rated and operations in 64 countries. Its patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting.