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We are looking for a Data Science Team Lead to guide a team building production-ready machine learning and AI systems that support data-driven decision-making across the company.

This is a hands-on leadership role for someone who enjoys combining technical depth, product thinking, and people development. You will work closely with Data Analysis, Data Engineering, Data Warehousing, product teams, and business stakeholders to turn complex business needs into reliable, scalable, and measurable data science solutions.

You will lead work across the full ML and AI lifecycle, from problem framing and experimentation to deployment, monitoring, and continuous improvement. The team works with a practical modern stack including Python, SQL, Airflow, GCP, Docker, Kubernetes, MLflow, Langfuse, FastAPI, BentoML, LlamaIndex, LangChain, Haystack, Milvus, and related tools.

 

What You’ll Do

• Lead the Data Science team in building reliable ML and AI solutions, from problem framing and data exploration to deployment, monitoring, and continuous improvement.
• Translate business needs into clear technical plans, measurable success criteria, and practical solutions with visible business impact.
• Work closely with Data Analysis, Data Engineering, Data Warehousing, product, and business stakeholders to design scalable data and model workflows.
• Set technical standards for clean code, reproducible experiments, model tracking, documentation, observability, and production readiness.
• Mentor data scientists, support their growth, and help the team make strong technical and product decisions.

 

What You’ll Bring

• 5+ years of experience in applied ML/AI, including hands-on work with production-oriented solutions.
• Strong Python and SQL skills, with experience working with complex real-world data and building maintainable workflows.
• Experience with the ML lifecycle, including feature engineering, model training, evaluation, deployment, monitoring, and iteration.
• Practical exposure to LLM-based applications, such as RAG, embeddings, prompt engineering, vector search, orchestration, or evaluation.
• Strong communication and leadership skills, with the ability to explain technical trade-offs and guide both people and projects.

 

Nice to Have

• Master’s degree or PhD in Computer Science, Mathematics, Statistics, Data Science, Machine Learning, or a related field.
• Experience with tools such as GCP, Airflow, Docker, Kubernetes, MLflow, Langfuse, FastAPI, BentoML, LlamaIndex, LangChain, Haystack, Milvus, or similar.
• Familiarity with MLOps, model governance, observability, data quality monitoring, or reusable ML/AI platform components.

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