Senior Data Scientist

Securityscorecard

Canada

Remote

Who this role is best for

A natural match if you have experience in cybersecurity analytics and deploying machine learning models.

Best fit for

Candidates with 5+ years of applied machine learning and experience in cybersecurity or risk domains
— “5+ years of experience applying data science and machine learning to real-world challenges.”
Individuals who have deployed models in cloud environments and worked with data pipelines
— “Experience working with cloud-based data pipelines (AWS, GCP, or similar).”
Professionals skilled in Python and ML frameworks with a track record of model evaluation and validation
— “Proficient in Python and ML/data science frameworks (e.g., scikit-learn, XGBoost, MLFlow, PyTorch).”

Things to consider

The role requires a strong ability to communicate insights to non-technical stakeholders
— “Communicate findings and recommendations effectively to both technical and non-technical audiences.”
Prior exposure to LLMs is not required but could be a differentiator
— “Prior exposure and interest in LLMs is a plus.”

How to stand out

Highlight specific examples of deploying models into production systems
— “Collaborate with product managers and engineers to define data science requirements and integrate models into production systems.”
Demonstrate experience in evaluating and validating models for accuracy and scalability
— “Evaluate and validate models, ensuring their accuracy, robustness, and scalability.”
Showcase a history of working with large, complex datasets and deriving actionable insights
— “Perform exploratory data analysis and identify patterns, trends, and anomalies in large and complex datasets.”
Emphasize your ability to work cross-functionally and contribute to team best practices
— “Contribute to code reviews, design discussions, and data science best practices.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Developed and deployed machine learning models
Improved cybersecurity ratings platform

Typical background

Advanced degree in quantitative field5+ years of data science experience

Skills & requirements

Required

PythonMachine LearningData AnalysisCloud-based Data PipelinesModel Deployment

Preferred

CybersecurityLlmsScala

Stack & domain

Pythonscikit-learnXgboostMlflowPyTorchAWSGCPML AlgorithmsEvaluation MetricsModel DeploymentCommunicationTeam PlayerCybersecurityThreat IntelligenceRisk Scoring

About the role

Original posting from Securityscorecard via Greenhouse

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 $150,000 - $175,000 (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

Source: Securityscorecard careers (Greenhouse)