Manager Data Scientist - AI

Applied AI Tribe

Role

People leadership

• Lead, grow, and retain data scientists; set quality expectations and support career development.
• Partner with recruiting, mentor the team, and run planning, design reviews, and retrospectives.

Technical strategy and delivery

• Scope business problems into ML and AI solutions with measurable success criteria; own priorities across the team's roadmap.
• Establish evaluation pipelines, LLM observability, tooling, and infrastructure; use golden datasets and stakeholder-aligned metrics in product iterations.
• Oversee data pipelines, feature engineering, model training, deployment, serving, and monitoring.
• Drive LLM and generative AI applications for data enrichment, content understanding, and automated decision-making.
• Design and analyze A/B and multivariate experiments; improve MLOps, code quality, tooling, and internal learning.

Cross-functional partnership

• Partner with product managers and business teams to identify and shape high-value AI opportunities.
• Communicate problems, results, and recommendations to senior stakeholders; collaborate with engineering on data systems, reliable models, and production integration.

Technical qualifications

• Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
• Hands-on framework knowledge includes Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
• Proficiency with the OpenAI SDK and major LLM provider APIs; hands-on LangGraph experience for agentic workflows.
• Hugging Face experience with model sourcing, fine-tuning, and deployment; understanding of embeddings, retrieval, vector databases, semantic search, and RAG.
• Strong production software engineering, ML system design, MLOps, model monitoring, data pipeline, and feature engineering skills.
• Excellent SQL and Python; statistical foundations in experiment design, causal inference, and predictive methods.
• Agentic system design, BigQuery, Google Cloud Platform, and experience in online consumer products are described as pluses; Airflow is given as an example pipeline tool.

Education and experience

• Bachelor's degree in Engineering, Computer Science, or a related field; a postgraduate degree is a plus.
• At least 6 years of data science, ML engineering, and/or generative AI experience, including production model delivery.
• At least 2 years managing or leading a data science or ML team, with a track record of developing talent and delivering through others.

Ways of working