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 focused on building and delivering machine learning (ML) and generative AI systems that support decisions across the organization's products and business. This role requires you to be both a technical expert and a people leader, ensuring effective team growth while owning the delivery and roadmap of AI initiatives.

About the Applied AI Tribe
The Applied AI Tribe functions as the internal AI engine of the organization, dedicated to turning cutting-edge AI into tangible business value. The team has successfully shipped over 40 AI products across several strategic streams, including:

• Product & Tech Platform: Integrating AI capabilities into the core product and engineering platform.
• Business Automations: Creating streamlined workflows to save time and resources in operations.
• Self-Serve AI Tools: Enabling team members to utilize AI independently without tickets.
• Training & Onboarding: Upskilling team members to engage with AI technologies across the organization.
  The engineering philosophy focuses on delivering agentic AI systems at scale with robust evaluation and feedback mechanisms integrated into each product iteration.

What's On Your Plate?
People Leadership:

• Lead, develop, and retain a team of data scientists, setting high standards for technical quality and promoting individual career growth.
• Collaborate with recruitment to attract top talent in data science and ML as the team expands.
• Mentor team members in ML best practices, system design, production engineering, and stakeholder engagement.
• Facilitate team rituals, planning, design reviews, and retrospectives to maintain alignment and momentum.

Technical Strategy & Delivery:

• Transform ambiguous business challenges into clear, scoped AI solutions with defined success criteria aligned with the tribe’s goals.
• Manage the technical roadmap by prioritizing impactful projects related to data enrichment, business automation, self-serve tools, and intelligent product features.
• Advocate for a harness-first approach to ensure foundational evaluation frameworks and infrastructure are in place before building agent logic.
• Incorporate rigorous evaluation in every product iteration, aiming for consistent improvements in metrics and quality.
• Oversee the complete ML lifecycle, from data pipelines and feature engineering to model deployment and monitoring.
• Promote the use of large language models (LLMs) and generative AI for data enrichment and decision automation at scale.
• Design and conduct experiments to thoroughly assess product and model impact.
• Elevate ML and engineering quality through improved MLOps, code practices, tooling, and internal educational initiatives.

Cross-functional Partnership:

• Work with product managers and other business teams to identify valuable AI opportunities that shape the team's focus.
• Clearly communicate with senior stakeholders about problem framing, results, and recommendations.
• Collaborate with engineering teams to enhance understanding of data systems and ensure effective production integration.

Qualifications
What Did We Order?
Technical Experience:

• Expertise in machine learning, generative AI, deep learning, natural language processing (NLP), recommendation systems, and data mining.
• Practical experience with ML frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, and Transformers, including LLM fine-tuning.
• Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for production AI solutions.
• Experience utilizing LangGraph for developing complex agentic workflows and orchestration.
• Experience with Hugging Face for model sourcing, fine-tuning, and deployment.
• Understanding of embeddings, retrieval methods, vector databases, and semantic search for building research augmentation (RAG) pipelines.
• Solid software engineering skills, including clean code practices and ML system design.
• Proven experience with deploying and monitoring ML models in production environments, along with MLOps knowledge.
• Strong data and ML engineering skills for constructing and orchestrating data pipelines (e.g., using Airflow).
• Proficiency in SQL and Python; understanding of statistical concepts including experiment design and causal inference.
• Familiarity with agentic system design is an advantage, as is experience with BigQuery and Google Cloud Platform.

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 deploying models in a production setting.
• 2+ years of experience managing or leading a data science or ML team, demonstrating talent development and team delivery.
• Experience building ML systems for online consumer products is a strong plus.

Mindset & Ways of Working

• Embrace curiosity and a willingness to experiment with new models and frameworks.
• Take ownership of cost, latency, and business impact within your team's projects.
• Favor building prototypes over perfect design documents.
• Adapt comfortably to ambiguity, contributing to the development of new processes.
• Simplify approaches to maximize value while minimizing complexity.