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 deploying ML and generative AI systems that enhance decision-making across the company's product and business landscape. You will balance your role as a people leader and a technical practitioner, ensuring high-quality decisions while managing the team's roadmaps and delivery. Your responsibilities encompass the full AI lifecycle, from identifying ambiguous business problems to model training, deployment, and production monitoring, with a significant emphasis on leveraging LLMs and generative AI for decision automation and intelligent experience creation.

What's On Your Plate?
People Leadership:

• Lead, grow, and retain a team of data scientists while fostering a culture of ownership.
• Partner with recruiting to attract top data science and ML talent.
• Mentor team members in ML best practices and stakeholder communication.
• Conduct effective team rituals for alignment and progress.

Technical Strategy & Delivery:

• Translate business problems into well-defined ML and AI solutions with measurable success criteria.
• Own the technical roadmap by prioritizing impactful work across various AI domains.
• Champion harness-first thinking and ensure evaluation pipelines are established prior to building agent logic.
• Embed evaluation in product iterations to inform development priorities.
• Oversee the full ML lifecycle, including data pipelines, feature engineering, model training, and monitoring.
• Drive the adoption of LLMs and generative AI for smart decision-making and content understanding at scale.
• Design experiments to rigorously assess model and product impact.
• Elevate ML and engineering standards within the team, enhancing MLOps practices and tooling.

Cross-functional Partnership:

• Collaborate with product managers and business teams to identify AI opportunities.
• Clearly communicate with senior stakeholders regarding problem framing and results.
• Partner with engineering teams for reliable data models and integration support.

Qualifications
What Did We Order?
Technical Experience:

• Deep expertise in machine learning, generative AI, deep learning, NLP, and recommendation systems.
• Hands-on experience with ML frameworks such as Scikit-learn, PyTorch, TensorFlow, and fine-tuning LLMs.
• Proficiency with the OpenAI SDK and major LLM provider APIs.
• Experience using LangGraph for agentic workflows and Hugging Face for model deployment.
• Understanding of embeddings and semantic search for creating RAG pipelines.
• Strong software engineering fundamentals with experience monitoring ML models in production.
• Skills in data pipeline orchestration and feature engineering are essential.
• Proficient in SQL and Python, with a solid base in statistical methods and experiment design.
• Familiarity with agentic system design is a strong plus.
• 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.
• 6+ years in data science, ML engineering, or generative AI, including deploying models.
• 2+ years in a management role or leading a data science/ML team.
• Experience in building ML systems within an online consumer product environment is favorable.

Additional Information
Mindset & Ways of Working:

• Curiosity over certainty: eager to explore new models and frameworks.
• Ownership: accountable for cost, latency, and business impact.
• Bias to build: prioritize real workflow prototypes over idealized designs.
• Comfortable with ambiguity: ability to navigate undefined areas and contribute to new plays.
• Keep it simple: seek solutions that maximize value with minimal complexity.

At the company, success is measured by both results and the manner in which they are achieved. Excellence in craft, coupled with adherence to leadership principles, drives progress and enhances the overall experience.