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 the ML and generative AI systems that power decisions across the product and business. You are both a people leader and a technical practitioner, capable of making significant technical decisions, growing your team, and owning the roadmap and delivery. Your team’s work spans the full AI lifecycle—from framing ambiguous business problems through data modeling, feature engineering, model training, deployment, and production monitoring. A significant focus will be on leveraging LLMs, agentic systems, and generative AI to automate decisions, enrich data, and build intelligent experiences at scale, measured against Net AI Value Delivered.
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Key Responsibilities
People Leadership:
- Lead, grow, and retain a team of data scientists, setting a high bar for technical quality, fostering a culture of ownership, and supporting each person's career development.
- Partner with recruiting to attract top data science and ML talent as the team scales.
- Mentor team members in ML best practices, agentic system design, production engineering, and stakeholder communication.
- Run effective team rituals, planning, design reviews, and retrospectives that keep the team aligned and moving with pace.
Technical Strategy & Delivery:
- Translate ambiguous business problems into well-scoped ML and AI solutions with clear, measurable success criteria tied to the tribe's north star.
- Own the team's technical roadmap: prioritize high-impact work across data enrichment, business automation, self-serve tooling, and agentic product features.
- Champion harness-first thinking, ensuring evaluation pipelines, LLM observability, tooling, and infrastructure are in place before building agent logic.
- Embed evaluation into every product iteration: using golden datasets, stakeholder-aligned metrics, and weekly evaluation jobs that guide development cycles.
- Oversee the full ML lifecycle: data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
- Drive adoption 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 measure model and product impact rigorously.
- Elevate ML and engineering standards across the team, improving MLOps, code quality, tooling, and internal learning programs.
Cross-Functional Partnership:
- Partner with product managers and business teams to identify high-value AI opportunities and shape them into the team's roadmap.
- Communicate clearly with senior stakeholders, from problem framing through to results and recommendations.
- Collaborate with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration.
Qualifications
Technical Experience
- Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
- Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
- Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for production AI systems.
- Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
- Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment.
- Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, for building RAG pipelines and similarity-based applications.
- Strong software engineering fundamentals: clean production code, data structures and algorithms, and ML system design.
- Proven experience shipping and monitoring ML models in production, with a solid grasp of MLOps practices.
- Strong data and ML engineering skills: building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
- Excellent SQL and Python proficiency; solid statistical foundations, including experiment design, causal inference, and predictive methods.
- Familiarity with agentic system design is a strong plus.
- Experience with BigQuery and Google Cloud Platform is also a plus.
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, and/or generative AI, including shipping models to production.
- 2+ years managing or leading a data science or ML team, with a track record of developing talent and delivering through others.
- Experience building ML systems in an online consumer product environment is a strong plus.
Mindset & Ways of Working
- Curiosity over certainty: excited to try new models or frameworks.
- Ownership: treat cost, latency, and business impact as a personal responsibility.
- Bias to build: prioritize prototypes over perfect design documentation.
- Comfortable with ambiguity: help create the playbook when none exists.
- Keep it simple: choose approaches that deliver the most value with the least complexity.
