• Lead and architect the research strategy for applying AI to cybersecurity. • Drive innovation in AI applications, including novel approaches to reasoning, summarisation, and interactivity • Make strategic decisions about GenAI model architectures, training approaches, and deployment strategies • Implement cutting edge ML/AI research in the context of cybersecurity • Build production ready AI systems that operate at CrowdStrike’s massive scale. • Mentor and guide scientists while fostering a culture of engineering excellence • Lead cross-functional initiatives and collaborate with senior leadership to align technical strategy with business goals • Represent CrowdStrike's technical excellence through publications, speaking engagements, and thought leadership • Establish best practices, technical standards, and evaluation frameworks for the team

Role Description We are seeking a dynamic and innovative Senior Data Scientist to join our forward-thinking team. In this pivotal role, you will leverage data to drive operational excellence and spearhead the development of sophisticated machine learning models. Collaborate with top-tier Machine Learning and Data Engineers to bring these models into production and provide AI expertise across the organization, ensuring data-driven decision-making and innovative solutions. Qualifications - 5+ years of hands-on experience in data science and machine learning, with a proven portfolio of impactful projects. - Experience in training tabular and time-series models using classical machine learning and deep learning approaches. - Proficiency in building data pipelines, training models, and conducting experiments using industry-standard tools. - Advanced SQL skills, including the ability to debug complex queries and a solid understanding of OLTP vs. OLAP systems (experience with OLAP systems is a plus). - Exceptional analytical skills with a detail-oriented and innovative approach to challenges. - Strong verbal and written communication skills to collaborate effectively within a team. Requirements - Experience with cloud platforms (AWS, GCP, Azure) and container tools like Kubernetes. - Expertise in ML pipeline development, including data preprocessing, model training, deployment, and monitoring. - Familiarity with MLOps tools like MLflow or Feast. - Hands-on experience with large-scale OLAP systems such as Clickhouse, BigQuery, or Snowflake. - Knowledge of advanced techniques for handling imbalanced datasets and other complex modeling challenges. Responsibilities - Design, execute, and evaluate experiments to refine models and improve outcomes. - Create, validate, and maintain state-of-the-art algorithms, focusing on both tabular and time-series data. - Perform deep-dive data analysis and deliver actionable insights to shape our business strategies. - Work closely with cross-functional teams to align data science projects with company goals. - Keep up with the latest advancements in data science, cloud technologies, and DevOps. - Write, debug, and optimize complex SQL queries while effectively navigating OLAP and OLTP systems. Benefits - Competitive salary (€6,500 - €9,000 gross, depending on the level of experience). - Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. - Enjoy a flexible, remote-first global environment. - Equity options. - Private health insurance. - Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. - Spend 10% of your work time on personal projects or self-improvement. - Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. - Annual hackathon to spark new ideas and strengthen team bonds. - Team-building budget and company events to connect with your colleagues. - Equipment budget to ensure you have everything you need. - Extra days off to help maintain a healthy work-life balance.

• Develop new business and expand market presence in Data Center markets • Drive revenue growth through proactive customer engagement and opportunity development • Promote cross-brand solutions and build strong customer relationships • Gather and communicate market insights and competitive intelligence • Collaborate with Product Management, Marketing, and Inside Sales

• Oversee maintenance of data infrastructure and architecture for Oil, Gas, and Remediation projects • Drive data generation, storage, and processing initiatives while ensuring the implementation of effective data management solutions and best practices • Maintain databases and data processing systems • Ensure data is accessible, reliable, secure, and efficiently processed • Combine, optimize, and manage multiple large-scale data sources • Create automated reporting systems and data solutions • Provide software solutions that move data from its source to internal databases, including data warehouses • Responsible for maintenance of data pipelines to support data analysis and reporting • Analyze complex issues and recommend solutions that improve data storage processes and methodologies • Use judgment outside defined parameters and precedents to make decisions and resolve business and technical challenges • Accountable for the results of assigned projects and processes • Demonstrate strong proficiency in data programming languages • Demonstrate strong knowledge of data optimization tools, data structures, data architecture, and data warehousing solutions • Demonstrate strong knowledge of API integration to support data access, collection, distribution, transformation, and ELT processes • Apply algorithmic and statistical methods, data processing tools, and AI-enabled approaches to support efficient data consumption and analysis