• Build a comprehensive map of the decision-making chain within target clients • Drive partnership upgrades with Nvidia or OpenAI and Colo clients through joint POCs • Align technical roadmap and solutions to client needs • Identify alignment opportunities for Midea’s cooling solutions

• Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance, driving measurable revenue and media efficiency. • This is a hands-on, in-the-weeds role: you are heavily immersed in the data and the modeling, framing the business problem directly with stakeholders, building and validating the model, handing the ML-engineering last mile to your ML engineering partner, and staying engaged through deployment, monitoring, and performance analysis. • You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time. Your primary metric is ROAS. • Deliver buying models that maintain positive ROAS and quality. • Drive lead quality improvements across our portfolio of brands: Messaging, Funnels, Content/Listicles, and more resulting in measurable impact to revenue growth. • Establish trusted, direct partnership with vertical business stakeholders. • Produce trusted output: validated, documented, low correction burden. • Identify and leverage net-new modeling opportunities the business has not flagged.

• Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance, driving measurable revenue and media efficiency. • This is a hands-on, in-the-weeds role: you are heavily immersed in the data and the modeling, framing the business problem directly with stakeholders, building and validating the model, handing the ML-engineering last mile to your ML engineering partner, and staying engaged through deployment, monitoring, and performance analysis. • You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time. • Your primary metric is ROAS. • Deliver buying models that maintain positive ROAS and quality • Drive lead quality improvements across our portfolio of brands: Messaging, Funnels, Content/Listicles, and more resulting in measurable impact to revenue growth. • Establish trusted, direct partnership with vertical business stakeholders. • Produce trusted output: validated, documented, low correction burden. • Identify and leverage net-new modeling opportunities the business has not flagged.

DescriptionThis role combines deep technical expertise with leadership responsibility, driving multidisciplinary Data Science initiatives end-to-end within complex, security policy management systems. The role drives the development of innovative, production-grade AI capabilities - including security AI agents and advanced machine learning models built on complex security data. Original thinking, deep technical rigor, intellectual agility, and exceptional problem-solving are essential. Key Responsibilities - Lead end-to-end Data Science initiatives from problem framing through validation, CI/CD-based production deployment, monitoring, and ongoing operational optimization of AI systems - Design and implement security-focused AI agents and reasoning systems - Develop advanced ML capabilities, including predictive modeling, anomaly detection, and classification - Adapt and fine-tune LLM technologies for domain-specific security use cases - Partner with Product, Engineering, and Security teams to deliver measurable business impact - Provide technical leadership and mentorship across multidisciplinary Data Science initiatives Requirements - M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline - At least 7 years of hands-on Data Science experience, delivering end-to-end solutions into production environments - Deep understanding of machine learning theory and practical model behavior - Strong expertise in Python and the modern Data Science ecosystem (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow, etc.) - Solid understanding of LLM architectures and fine-tuning methodologies - Excellent interpersonal skills and proven ability to work within multidisciplinary product teams - Strong analytical rigor and structured problem-solving capability Advantage - Experience with ML observability, model monitoring, or drift detection. - Experience with graph technologies, such as Neo4j. - Experience with Generative AI, RAG, GraphRAG, semantic search, vector databases, or domain-specific LLM fine-tuning / adaptation. - Experience designing and deploying AI agents or multi-step reasoning systems in on-premise environments. - Background in network security, firewall policies, or compliance analytics.