Senior Manager - Data Science

<p>Freshworks is looking for a strategic and technically proficient Senior Manager in our Applied AI team. This team serves as the "Architects of Excellence" for every AI initiative within the company. You will lead a high-impact squad of around 10 Data Scientists and AI Engineers responsible for ensuring that our AI agents and models are performant, secure, and continuously evolving.</p><p>As the primary stakeholder for AI design and deployment, your team’s mission is to bridge the gap between experimental AI and industrial-grade, customer-ready products. You will oversee the entire lifecycle of AI excellence—from initial design sign-offs and sophisticated evaluation frameworks to advanced model optimization and MLOps.</p><p>Responsibilities</p><p>Team Leadership &amp; Governance</p><ul><li><p>Lead and mentor a team of around 10 Data Scientists and AI Engineers, fostering a culture of technical rigor and continuous innovation.</p></li><li><p>Act as the primary stakeholder for all AI activities across the organization, reviewing architectural designs and providing the final technical sign-off.</p></li><li><p>Define and enforce standards for model selection strategies and effective prompting to ensure AI use cases are solved with the right tool for the job.</p></li></ul><p>Evaluation &amp; Quality Engineering</p><ul><li><p>Own the end-to-end Evaluation (Eval) strategy for AI use cases.</p></li><li><p>Architect and implement robust frameworks for both build-time evals and production-time evals to enable data-driven continuous learning loops.</p></li><li><p>Monitor production metrics for AI deployments (agents, models, and prompts) to identify and execute improvements.</p></li></ul><p>Model Optimization &amp; Compliance</p><ul><li><p>Drive the creation of top-tier models through fine-tuning, distillation, and Reinforcement Learning (RL) to achieve the optimal balance of price, performance, and power (reasoning).</p></li><li><p>Ensure all models are secure and that data training pipelines remain strictly compliant with global data privacy standards.</p></li></ul><p>MLOps &amp; Deployment Excellence</p><ul><li><p>Oversee sophisticated deployment strategies including shadow testing, A/B testing, and gradual rollouts.</p></li><li><p>Manage rollback protocols and conduct deep-dive field performance analysis of AI Agents to ensure seamless user experiences.</p></li></ul> <p>Qualifications</p><ul><li><p>Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).</p></li><li><p>Experience: 10+ years of professional experience in AI/ML with 2+ years in Agentic AI</p></li><li><p>Leadership: Minimum of 3+ years in a formal people management role, with a proven track record of scaling and mentoring technical teams.</p></li><li><p>Domain Expertise: Deep understanding of Large Language Models (LLMs), Generative AI lifecycles, and traditional ML foundations.</p></li></ul><p>Skills Inventory</p><p>Required Skills</p><ul><li><p>Model Engineering: Proficiency in model fine-tuning, distillation techniques, and RL/RLHF.</p></li><li><p>Evaluation Frameworks: Expertise in building automated eval pipelines for LLMs (e.g., using Arize, RAGAS, DeepEval, or custom benchmarking).</p></li><li><p>MLOps Mastery: Practical experience with shadow deployments, A/B testing, and managing production AI agents at scale.</p></li><li><p>Strategic Prompting: Advanced knowledge of prompt engineering patterns (Chain of Thought, ReAct, etc.).</p></li><li><p>Security &amp; Compliance: Strong grasp of AI security vulnerabilities (prompt injection, data leakage) and data compliance (GDPR/SOC2).</p></li></ul><p>Good-to-Have Skills</p><ul><li><p>Infrastructure: Familiarity with cloud-native AI infrastructure (AWS Bedrock, Azure AI, or GCP Vertex AI).</p></li><li><p>Resource Optimization: Experience in optimizing models for "Power" (reasoning capability) vs. "Price" (token cost) vs. "Performance" (latency).< ... (truncated, view full listing at source)