Senior Cyber Data Scientist, AVP What you will be responsible for
As a Cyber Data Scientist, you will:
Education & Qualifications Minimum Qualifications
- Use your understanding of Data Science, AI & Machine Learning applications to wrangle our unique cybersecurity data and create analyses and tools that point to the most significant business, governance, and risk management impacts.
- Work hand in hand with product owners and domain experts from across the Global Cybersecurity organization to develop novel analytics and ML solutions for critical identity, security, and risk management problems.
- Analyze large datasets using SQL and scripting languages to surface meaningful/actionable insights and opportunities to partner teams and other key stakeholders
- Approach problems from first principles, using a variety of statistical and mathematical modeling techniques to research and understand behaviors and interactions
- Work with data analytics engineers to log new, useful data sources as we expand our portfolio of security tools and platforms, and with data platform engineers to develop capabilities for data and model operationalization
- Build, forecast, and report on metrics that drive strategy and facilitate decision making for key security initiatives
- Build, manage, deploy, and monitor end-to-end analytical and machine-learning solutions to scale our cyber behavioral intelligence platform
- Build and share data visualizations and self-serve dashboards for your product team, and support planning, facilitation, and execution of regular communication and coordination across cross-functional teams
- PhD in a quantitative technical field with 2+ years of relevant industry experience OR Bachelor's degree in a quantitative field with a minimum of 5-8 years of industry experience.
- Direct relevant experience building ML models and analytics for cybersecurity, insurance, and other data intensive risk management related domains, structuring large volumes of operational and log data in cloud native analytics environments.
- Demonstrated ability to work as an independent contributor driving research and analyses from conception to implementation with minimal guidance.
- Experience with scripting and data analysis programming languages, such as Python or R and advanced proficiency with SQL and data visualization tools
- Familiarity with the modern data science tools such as Pandas, Scikit-Learn, XGBoost, TensorFlow/Keras, MLFlow, Jupyter Notebooks
- Experience with cohort and funnel analyses, population clustering and segmentation techniques, and a deep understanding statistical concepts related to experimental design, selection bias, probability distributions, and Bayesian inference
- Experience answering unstructured questions , driving data-driven solutions, and managing projects and tasks to a conclusion
- Direct experience in the cybersecurity industry building analytics, models and detections (minimum 1-2 years).
- Familiarity with statistical and ML models for graph analysis, risk modeling in the actuarial or financial domain.
- Deep understanding of tools and techniques for fraud modeling and anomaly detection, forecasting and time-series analysis, and adaptive and reinforcement learning techniques.
- Experience using batch and real-time feature stores, and developing coordinated batch, streaming and online model execution workflows.
- Experience with data ops and big data tools such as Spark, Spark Streaming, Presto/Trino, Kafka,, Snowflake within cloud environments such as AWS, GCP, and Azure.
- Experience with MLOps and iterative cycles of end-to-end development, MRM coordination, deployment and monitoring of production grade ML models in a regulated high-growth tech environment