Applied AI Manager

About the Role
As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists responsible for building and shipping machine learning (ML) and generative AI systems that empower decisions across the organization’s product and business. Balancing your role as both a technical practitioner and people leader, you will guide your team towards achieving high technical standards while managing the roadmap and delivery of AI initiatives. Your team's responsibilities encompass the full AI lifecycle, from defining complex business problems to data modeling, feature engineering, model training, deployment, and monitoring in production. Your focus will include leveraging large language models (LLMs), agentic systems, and generative AI to automate decisions, augment data, and create intelligent experiences at scale, all measured against the Net AI Value Delivered.

What's On Your Plate?

• People Leadership:

  • Lead, grow, and retain a team of data scientists, fostering high technical quality and supporting career development.
  • Collaborate with recruiting to attract top data science and ML talent.
  • Mentor team members on ML best practices, agentic system design, and effective communication with stakeholders.
  • Conduct team rituals and planning sessions to ensure alignment and momentum.

• Technical Strategy & Delivery:

  • Transform ambiguous business challenges into clear ML and AI solutions with measurable success criteria aligned to the tribe's objectives.
  • Own the team's technical roadmap, prioritizing impactful work on data enrichment, business automation, and agentic product features.
  • Advocate for a harness-first thinking approach, ensuring evaluative frameworks and infrastructure are established before building solutions.
  • Integrate evaluation processes with each product iteration, including stakeholder metrics and weekly evaluation jobs to guide development.
  • Oversee the full ML lifecycle: data pipelines creation, feature engineering, model training, deployment, and monitoring.
  • Champion the adoption of LLMs and generative AI for data enrichment and smart decision-making.
  • Design and analyze A/B and multivariate experiments to rigorously evaluate model and product impact.
  • Promote ML and engineering standards, improving MLOps, code quality, and internal learning programs.

• Cross-functional Partnership:

  • Collaborate with product managers and business teams to identify key AI opportunities and translate them into the team’s roadmap.
  • Clearly communicate with senior stakeholders, presenting problems, results, and recommendations effectively.
  • Work alongside engineering teams to comprehend data systems, create reliable data models, and ensure effective production integration.

Qualifications

• Technical Experience:

  • Deep expertise in machine learning, generative AI, NLP, recommendation systems, and data mining.
  • Proficiency in ML frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, and Transformers, with hands-on experience in LLM fine-tuning.
  • Familiarity with OpenAI SDK and major LLM provider APIs for developing and managing production AI systems.
  • Experience with LangGraph for creating stateful, multi-step agentic workflows.
  • Knowledge of Hugging Face for model sourcing, fine-tuning, and deployment.
  • Solid concepts of embeddings, retrieval, vector databases, and semantic search for creating pipelines and similarity applications.
  • Strong software engineering skills balanced with data and ML engineering capabilities.
  • Proficient in SQL and Python with a strong foundation in statistics including experiment design and predictive methods.
  • Experience with agentic system design is a plus.
  • Experience with BigQuery and Google Cloud Platform is advantageous.

• Additional Qualifications:

  • Bachelor's degree in Engineering, Computer Science, or a related field; postgraduate degree is preferred.
  • 6+ years of experience in data science, ML engineering, or generative AI, with proven track record of production model delivery.
  • 2+ years of experience managing or leading a data science or ML team, demonstrating talent development and effective delivery through teams.
  • Experience building ML systems in a consumer product environment is a significant advantage.

Mindset & Ways of Working:

• Foster curiosity and initiative in exploration of new models and frameworks.
• Cultivate ownership over business impact and operational efficiencies.
• Prioritize action over perfection; prototype execution is preferred over exhaustive planning.
• Thrive in ambiguous situations by crafting innovative solutions.
• Simplify complexities to deliver maximum value efficiently.

**At this organization, your success is measured by both outcomes and the behaviors that lead to achievement. They emphasize excellence in craftsmanship and adherence to leadership principles that shape decision-making and collaboration, enhancing both individual and collective progress towards delivering exceptional benefits to the business and its customers.