Senior Data Scientist

Responsibilities

1. Understand complex cybersecurity and business problems, translate them into well-defined data science problems, and build scalable solutions.
2. Develop and deploy scalable, production-grade AI/ML systems for real-time threat detection and response.
3. Drive end-to-end ML lifecycle: from data ingestion and feature engineering to model development, evaluation, and deployment.
4. Analyse large and complex datasets generated by M365 to identify patterns and anomalies indicative of security risks.
5. Collaborate with security experts to understand threat landscapes and incorporate domain knowledge into models.
6. Continuously monitor and improve the performance of ML systems to adapt to evolving threats.
7. Lead the design and implementation of data-driven security solutions and tools.
8. Mentor and guide junior data scientists in best practices and advanced techniques.
9. Communicate findings and insights to stakeholders, including senior leadership and technical teams.

Qualifications

1. Experience in developing and deploying machine learning models for security applications.
2. Experience in Big Data preferably in the cybersecurity. Experience with data science workloads with the Azure tech stack; Synapse, Azure ML, etc.
3. Knowledge of anomaly detection, fraud detection, and other related areas. Familiarity with security fundamentals and attack vectors Publications or contributions to the field of data science or cybersecurityDoctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
4. 5+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
5. Experience in programming languages such as Python, R, or Scala, with hands-on experience in data analysis, experimental design principles and visualization.
6. Experience in translating complex data into actionable insights and recommendations that drive business impact.
7. Excellent technical design skills and proven ability to drive large scale system designs for complex projects or products.
8. Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
9. In-depth knowledge of cybersecurity principles, threats, and attack vectors.
10. Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and data processing.
11. Strong analytical and problem-solving skills with the ability to think creatively.
12. Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders.

Preferred Qualifications:

1. Experience in Experience in developing and deploying machine learning models for security applications. Experience in Big Data preferably in the cybersecurity. Experience with data science workloads with the Azure tech stack; Synapse, Azure ML, etc. Knowledge of anomaly detection, fraud detection, and other related areas. Familiarity with security fundamentals and attack vectors Publications or contributions to the field of data science or cybersecuritydeveloping and deploying machine learning models for security applications.
2. Experience in Big Data preferably in the cybersecurity.
3. Experience with data science workloads with the Azure tech stack; Synapse, Azure ML, etc.
4. Knowledge of anomaly detection, fraud detection, and other related areas.
5. Familiarity with security fundamentals and attack vectors Publications or contributions to the field of data science or cybersecurity