Applied AI Manager

About the Role
As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists tasked with building and shipping the ML and generative AI systems that power decisions across the product and business. Your role will require you to be both a people leader and a technical practitioner, enabling you to make pivotal technical decisions while nurturing and growing your team. You will oversee the entire AI lifecycle—from framing ambiguous business problems to data modeling, feature engineering, model training, deployment, and production monitoring. A significant focus of your work will be on leveraging LLMs, agentic systems, and generative AI to automate decisions, enrich data, and build intelligent experiences at scale, all measured against Net AI Value Delivered.

What's On Your Plate?
People Leadership:

• Lead, grow, and retain a team of data scientists, ensuring high standards for technical quality and fostering a culture of ownership.
• Partner with recruiting to attract top talent in data science and ML as the tribe scales.
• Mentor team members on ML best practices, agentic system design, production engineering, and stakeholder communication.
• Conduct effective team rituals, planning, design reviews, and retrospectives to keep the team aligned and moving at pace.

Technical Strategy & Delivery:

• Translate complex business problems into well-defined ML and AI solutions with clear, measurable success criteria that align with the tribe's objectives.
• Own the team's technical roadmap including prioritization of high-impact work across data enrichment, business automation, self-serve tooling, and agentic product features.
• Champion harness-first thinking and ensure necessary evaluation pipelines, LLM observability, tooling, and infrastructure are established before building agent logic.
• Embed evaluation into every product iteration, utilizing golden datasets, stakeholder-aligned metrics, and weekly evaluation jobs to inform development priorities.
• Oversee the entire ML lifecycle: managing data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
• Drive the adoption of LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making at scale.
• Design and analyze experiments (A/B and multivariate) to rigorously evaluate model and product impact.
• Elevate ML and engineering standards within the team, enhancing MLOps, code quality, tooling, and internal learning programs.

Cross-functional Partnership:

• Collaborate with product managers and business teams to identify high-value AI opportunities and develop them into the team’s roadmap.
• Communicate effectively with senior stakeholders, from problem framing to results and recommendations.
• Work closely with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration.

Qualifications
Technical Experience:

• Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
• Proficiency with ML and GenAI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
• Knowledge of the OpenAI SDK and familiarity with major LLM provider APIs for the production of AI systems.
• Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
• Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment.
• Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search for RAG pipelines and similarity-based applications.
• Strong software engineering fundamentals, including clean production code, data structures, algorithms, and ML system design.
• Proven experience in shipping and monitoring ML models in production and an in-depth understanding of MLOps practices.
• Strong data and ML engineering skills, including experience in building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
• Excellent skills in SQL and Python with solid statistical foundations, including experiment design, causal inference, and predictive methods.
• Familiarity with agentic system design principles is a strong plus.
• Experience with BigQuery and Google Cloud Platform is an advantage.

Additional Qualifications:

• Bachelor's degree in Engineering, Computer Science, or a related field. A postgraduate degree is a plus.
• 6+ years of experience in data science, ML engineering, or generative AI, including shipping models to production.
• 2+ years in a managerial role, leading a data science or ML team, with a proven track record of developing talent and delivering through others.
• Experience in building ML systems in an online consumer product environment is a strong advantage.

Mindset & Ways of Working:

• A curious mindset: you're excited to explore new models or frameworks.
• A sense of ownership: you consider cost, latency, and business impact as shared responsibilities.
• A bias for action: you value prototypes in real workflows over perfect documentation.
• Comfort with ambiguity: you contribute to creating playbooks where none exist.
• A focus on simplicity: you strive for approaches that deliver maximum value with minimal complexity.