Data Science Manager - Applied AI

About the Role
As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists in building and shipping ML and generative AI systems that enhance decision-making across the organization’s product and business functions. This role places you as both a people leader and a technical practitioner, allowing you to make important technical decisions, develop your team, and take ownership of the roadmap and delivery. Your team's efforts will cover the entire AI lifecycle—from defining unclear business problems to data modeling, feature engineering, model training, deployment, and ongoing monitoring. A key focus will be on utilizing LLMs, agentic systems, and generative AI to automate decisions, enrich data, and create intelligent experiences at scale, with performance measured against Net AI Value Delivered.

What's On Your Plate?
People Leadership:

• Lead, grow, and retain a team of data scientists while maintaining high standards for technical quality.
• Foster a culture of ownership and guide each team member's career development.
• Collaborate with recruiting to attract top data science and ML talent as the tribe expands.
• Mentor team members in ML best practices, agentic system design, production engineering, and effective stakeholder communication.
• Conduct effective team rituals, including planning, design reviews, and retrospectives, to keep the team aligned and progressing at pace.

Technical Strategy & Delivery:

• Convert ambiguous business challenges into clearly defined ML and AI solutions with measurable success criteria aligned with the tribe's north star.
• Own the technical roadmap for your team, prioritizing impactful work in areas such as data enrichment, business automation, self-serve tooling, and agentic product features.
• Promote harness-first thinking by ensuring evaluation pipelines, LLM observability, tooling, and necessary infrastructure are established before developing agent logic.
• Implement evaluation processes into every product iteration, including creating golden datasets and stakeholder-aligned metrics, with weekly evaluation jobs guiding development decisions.
• Supervise the full ML lifecycle, which encompasses data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
• Advocate for the use of LLMs and generative AI for data enrichment, smart content understanding, and large-scale automated decision-making.
• Design and evaluate experiments (A/B and multivariate) to accurately assess model and product impact.
• Advance ML and engineering standards throughout the team, enhancing MLOps, code quality, tooling, and internal training programs.

Cross-functional Partnership:

• Collaborate with product managers and business teams to identify valuable AI opportunities and shape these into the team's strategic roadmap.
• Clearly communicate with senior stakeholders, from problem definition to delivery of results and recommendations.
• Work alongside engineering teams to understand data systems, construct reliable data models, and facilitate seamless production integration.

Qualifications
What Did We Order?

• Technical Experience:
  • Extensive expertise in machine learning, generative AI, deep learning, natural language processing, recommendation systems, and data mining.
  • Proficient in ML and GenAI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
  • Familiarity with the OpenAI SDK and major LLM provider APIs for creating and managing production AI systems.
  • Practical experience with LangGraph for creating stateful, multi-step agentic workflows and complex orchestration patterns.
  • Experience with Hugging Face tools for model sourcing, fine-tuning, and deployment.
  • Strong understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, specifically for building RAG pipelines and similarity-based applications.
  • Strong software engineering fundamentals including clean production code, data structures, algorithms, and ML system design.
  • Proven experience in deploying and monitoring ML models in production settings, with a solid understanding of MLOps practices.
  • Excellent data and ML engineering skills, including the ability to build and manage data pipelines (e.g., using Airflow) and implement robust feature engineering.
  • Proficiency in SQL and Python; solid statistical foundation including experiment design, causal inference, and predictive methods.
  • Familiarity with agentic system design principles is a significant advantage.
  • Experience with BigQuery and Google Cloud Platform is a plus.

Qualifications:

• Bachelor's degree in Engineering, Computer Science, or a related discipline; a postgraduate degree is an advantage.
• 6+ years of experience in data science, ML engineering, or generative AI, particularly involving production model delivery.
• 2+ years of experience managing or leading a data science or ML team, demonstrating a successful track record in talent development and delivering results through others.
• Experience in developing ML systems within an online consumer product framework is highly regarded.

Additional Information
Mindset & Ways of Working:

• Embrace curiosity over certainty; anticipate trying new models or frameworks regularly.
• Take ownership of costs, latency, and business impact as part of your role and that of your team.
• Prefer actual prototypes within workflows over perfect but impractical design documents.
• Adapt comfortably in uncertain situations; much of the work involves forging a new path.
• Simplify approaches to prioritize maximum value with minimal complexity, focusing on execution.

At our organization, your success is shaped by both your results and behaviors. We prioritize excellence in craft alongside leadership principles that influence our approach to thinking, decision-making, and collaboration. What you accomplish is significant, and the manner in which it is achieved defines our collective mission.