Manager Data Scientist - 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 shipping the machine learning (ML) and generative AI systems that drive decision-making across the organization's product and business operations. This role requires you to be both a people leader and technical practitioner, ensuring high-quality technical decisions while growing your team and managing the roadmap and delivery of projects. Your team's work encompasses the complete AI lifecycle, from identifying ambiguous business problems to data modeling, feature engineering, model training, deployment, and production monitoring. You will place significant emphasis on leveraging large language models (LLMs), agentic systems, and generative AI to automate decisions, enhance data enrichment, and create intelligent experiences at scale, with a focus on delivering measurable business value.

About the Applied AI Tribe
The Applied AI Tribe serves as the organization's internal AI engine, dedicated to transforming advanced AI technology into tangible business outcomes. The team aims to deliver Net AI Value and has successfully launched over 40 AI products across four strategic areas:

• Product & Tech Platform: Integrating AI capabilities into the core product and engineering platform.
• Business Automations: Creating streamlined workflows that enhance operational efficiency and reduce costs.
• Self-Serve AI Tools: Providing tools that empower team members to utilize AI without requiring external requests.
• Training & Onboarding: Upskilling the tribe to participate effectively in the AI-driven era.
  The engineering philosophy focuses on deploying scalable agentic AI systems, incorporating evaluation at each product iteration, and maintaining a continuous feedback loop to refine the product roadmap.

What's On Your Plate?
People Leadership:

• Lead, develop, and retain a team of data scientists, ensuring a high standard of technical quality and fostering a culture of ownership and career development.
• Collaborate with recruiting to attract premier data science and ML talent.
• Mentor team members in ML best practices, agentic system design, production engineering, and stakeholder communication.
• Conduct effective team meetings, including planning sessions, design reviews, and retrospectives, to keep the team aligned and progressing effectively.

Technical Strategy & Delivery:

• Translate complex business challenges into well-defined ML and AI solutions with clear success metrics aligned with the tribe's objectives.
• Own the technical roadmap for the team, focusing on high-impact work in data enrichment, business automation, self-serve tools, and innovative product features.
• Advocate for a harness-first approach, ensuring evaluation pipelines, observability for LLMs, and necessary infrastructure are established before deploying agent logic.
• Integrate evaluation processes into each product development cycle, creating golden datasets and stakeholder-aligned metrics to inform project direction.
• Supervise the full ML lifecycle including data pipelines, feature engineering, model training, production deployment, and ongoing monitoring.
• Promote the use of LLMs and generative AI for enhanced data enrichment and automated decision-making capabilities at scale.
• Design experiments (like A/B testing) to rigorously assess model and product impacts.
• Drive improvements in ML and engineering practices across the team, including MLOps, code quality, tooling, and internal learning initiatives.

Cross-functional Partnership:

• Collaborate with product managers and business teams to recognize high-value AI opportunities and integrate them into the team’s roadmap.
• Communicate effectively with senior stakeholders, providing clear narratives from problem identification through to results and recommendations.
• Work closely with engineering teams to grasp data systems, create reliable data models, and ensure seamless production integration.

Qualifications
What Did We Order?
Technical Experience:

• Profound expertise in machine learning, generative AI, deep learning, natural language processing (NLP), recommendation systems, and data mining.
• Practical experience with ML and GenAI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and fine-tuning LLMs.
• Familiarity with the OpenAI SDK and major LLM provider APIs necessary for developing production AI systems.
• Experience using LangGraph for constructing multi-step workflows and orchestrating complex agent interactions.
• Proficient in Hugging Face tools for model sourcing, fine-tuning, and deployment.
• Solid understanding of embeddings, dense and sparse retrieval, vector databases, and techniques for semantic search needed for RAG pipelines and similarity applications.
• Strong background in software engineering including clean production code practices, data structures, algorithms, and ML system design.
• Proven experience in deploying and continuously monitoring ML models in production, coupled with a strong understanding of MLOps principles.
• Solid data engineering skills, capable of building and managing data pipelines (e.g., using Airflow) and robust feature engineering.
• Advanced experience with SQL and Python; a solid grounding in statistical methodologies including experimental design and predictive modeling.
• Familiarity with agentic system design and best practices around harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops is desirable.
• Experience with BigQuery and Google Cloud Platform is a plus.

Qualifications:

• Bachelor's degree in Engineering, Computer Science, or a related area; a postgraduate degree is an advantage.
• Over six years of experience in data science, ML engineering, and/or generative AI, including practical experience in deploying models.
• At least two years in a managerial role within data science or ML, demonstrating a track record of team development and successful project delivery.
• Experience in developing ML systems for consumer-facing products is highly desirable.

Additional Information
Mindset & Ways of Working:

• Curiosity over certainty: an eagerness to experiment with new models and frameworks continuously.
• Ownership: accountability for costs, latency, and business outcomes as both personal and team responsibilities.
• Bias toward building: prioritizing prototyping in real scenarios over perfect documentation.
• Comfort with ambiguity: embracing the unknowns in project development and contributing to a developing framework.
• Simplicity: focusing on approaches that deliver the maximum value with minimal complexity.