Lead Applied Scientist

📋 Description

• • As the Lead Applied Scientist at AirOps, you will be at the forefront of shaping how brands achieve dominance in the evolving landscape of AI-driven search environments. This pivotal role demands a unique blend of profound technical expertise and strategic foresight, empowering you to architect and implement production-grade machine learning systems. These systems will directly influence how companies generate and refine content specifically for AI agents, ultimately enhancing their search visibility and driving measurable business outcomes.
• • You will operate at the dynamic intersection of Natural Language Processing (NLP), sophisticated search algorithms, and the transformative power of Large Language Models (LLMs). Your mission will be to engineer innovative solutions that enable content teams to achieve tangible business results, turning content quality into a sustainable competitive advantage in the age of AI.
• • This is a hands-on leadership position, requiring you to not only conceptualize and design complex systems but also to actively contribute through coding and implementation. You will forge strong partnerships with product management, engineering, and customer success teams, proactively identifying opportunities where advanced machine learning can revolutionize and elevate our platform's capabilities.
• • Your contributions will have a direct and significant impact on how thousands of brands navigate and succeed in the rapidly shifting search paradigm, where AI is increasingly dictating discovery and user engagement. You will be instrumental in building the intelligent systems that will define the next generation of marketing leadership.
• • Key Responsibilities:
• • Technical Leadership and System Architecture: Design, develop, and deploy end-to-end machine learning systems. This includes, but is not limited to, advanced NLP models for content understanding and generation, sophisticated search and recommendation algorithms to improve content discoverability, and innovative LLM-based applications for content optimization and analysis.
• • Productionalizing ML Models: Take machine learning models from the research phase through to robust production deployment. This involves meticulous optimization for low latency, cost-efficiency at scale, and ensuring high availability and reliability of deployed systems.
• • Search and Content Intelligence Development: Build and refine ML systems dedicated to analyzing AI search behavior patterns. Identify high-impact content opportunities by understanding how AI agents discover, rank, and interact with content. Develop predictive models to forecast content performance across various AI-driven platforms and search engines.
• • Algorithm Design for AI Content Optimization: Create and implement novel algorithms that empower brands to understand and optimize their content specifically for how AI agents interpret and rank it. This includes developing metrics and methodologies for AI-driven content evaluation.
• • Cross-functional Collaboration and Strategy: Partner closely with product managers to translate complex business requirements and user needs into actionable technical solutions and ML roadmaps. Work collaboratively with engineering teams to integrate ML components seamlessly into the AirOps platform.
• • Customer Impact and Insights: Collaborate with customer success to understand client challenges and identify how ML can provide unique solutions. Use data science to derive actionable insights from customer usage and content performance data, informing both product development and customer strategy.
• • Mentorship and Team Development: As a lead, contribute to the growth of the data science and ML team by sharing expertise, improving team practices, and mentoring junior scientists. Influence architectural decisions and champion best practices in ML development.
• • Innovation and Research: Stay abreast of the latest advancements in NLP, LLMs, search technologies, and applied machine learning. Propose and explore new research directions and technologies that can provide a competitive edge for AirOps and its customers.
• • Performance Monitoring and Iteration: Establish robust monitoring systems for deployed ML models to track performance, detect drift, and ensure ongoing accuracy and relevance. Drive iterative improvements based on performance data and evolving business needs.