Engineering Manager - Applied AI

About the Role
As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists responsible for building and shipping the ML and generative AI systems that influence decisions across the organization’s products and business. You will function as both a people leader and technical practitioner, engaging closely with your team's work to make significant technical decisions, grow their skills, and manage the roadmap and delivery. Your team will handle the entire AI lifecycle, from identifying ambiguous business problems to data modeling, feature engineering, model training, deployment, and production monitoring. A significant emphasis will be placed on leveraging large language models (LLMs), agentic systems, and generative AI to automate decisions, enrich data, and create intelligent experiences at scale, all while being evaluated against the Net AI Value Delivered.

What's On Your Plate?
People Leadership:

• Lead, grow, and retain a team of data scientists, setting a high standard for technical quality and nurturing a culture of ownership while supporting each team member's career development.
• Collaborate with recruiting to attract top talent in data science and machine learning as the tribe expands.
• Mentor team members on ML best practices, agentic system design, production engineering, and effective stakeholder communication.
• Facilitate team rituals such as planning, design reviews, and retrospectives to maintain alignment and pace.

Technical Strategy & Delivery:

• Translate ambiguous business problems into structured ML and AI solutions with clear, measurable success criteria linked to the tribe's objectives.
• Manage the technical roadmap for your team, prioritizing impactful work across data enrichment, business automation, self-serve tools, and agentic product features.
• Promote harness-first thinking and ensure that evaluation pipelines, LLM observability, tooling, and infrastructure are established before building agent logic.
• Integrate evaluation in every product iteration, focusing on golden datasets, stakeholder-aligned metrics, and regular evaluation jobs to guide development decisions.
• Oversee the entire ML lifecycle including data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
• Drive the use 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 assess model and product impact.
• Enhance ML and engineering standards within the team, focusing on MLOps, code quality, tooling, and internal learning programs.

Cross-functional Partnership:

• Collaborate with product managers and business teams to spot valuable AI opportunities and incorporate them into the team's roadmap.
• Effectively communicate with senior stakeholders from problem framing to providing results and recommendations.
• Work together with engineering teams to understand data systems, build reliable data models, and ensure seamless integration into production.

Qualifications
What Did We Order?
Technical Experience:

• Extensive expertise in machine learning, generative AI, deep learning, natural language processing (NLP), recommendation systems, and data mining.
• Proficient in ML and GenAI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and fine-tuning LLMs.
• Skilled with the OpenAI SDK and knowledgeable about major LLM provider APIs for constructing and managing production AI systems.
• Practical experience with LangGraph for developing stateful, multi-step agentic workflows and complex orchestration patterns.
• Familiar with Hugging Face (Transformers, Hub, Inference API) for sourcing, fine-tuning, and deploying models.
• Strong understanding of embeddings, both dense and sparse retrieval, vector databases, and semantic search for building retrieval-augmented generation (RAG) pipelines and similarity-based applications.
• Solid software engineering foundations: clean production code, data structures and algorithms, and ML system design.
• Proven record of shipping and monitoring ML models in production, with a strong grasp of MLOps practices.
• Adept in data and ML engineering, capable of building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
• Excellent command of SQL and Python; strong statistical grounding including experimental design, causal inference, and predictive methodologies.
• Familiarity with agentic system design principles (harness-first thinking, LLM observability, evaluation pipelines, production feedback loops) is highly desirable.
• Experience with BigQuery and Google Cloud Platform is advantageous.

Qualifications:

• Bachelor's degree in Engineering, Computer Science, or a related field; a postgraduate degree is a plus.
• At least 6 years of experience in data science, ML engineering, and/or generative AI, including deployment of models into production.
• Minimum of 2 years in a management or leadership role within a data science or ML team, with a history of talent development and delivering through others.
• Strongly preferred experience in building ML systems for online consumer products.

Additional Information
Mindset & Ways of Working:

• Emphasize curiosity over certainty: eager to explore new models or frameworks.
• Exhibit ownership: consider cost, latency, and business impact as personal responsibilities.
• Favor a bias to build: prioritize practical prototypes in workflows over perfect design documents.
• Remain comfortable with ambiguity: help navigate the undefined paths of your work.
• Prioritize simplicity: select approaches that yield maximum value with minimum complexity.