What you bring

You demonstrate a proactive approach to leveraging AI in everyday workflows, ensuring high‑quality outputs through thoughtful context design and system integration.
8+ years experience in data science, applied statistics, and machine learning, with end-to-end ownership from problem framing through deployment and value measurement.
Proven ability to lead and govern complex, business-critical initiatives in high-ambiguity, high-impact environments.
Proven ability to work in an Agentic AI context, shaping and applying AI agents that act autonomously within defined boundaries, collaborate with human stakeholders, and deliver measurable business outcomes in customer-centric environments.
Strong command of modern analytics and ML technologies, including advanced modeling, large-scale data processing, and AI-driven approaches (e.g. NLP, generative AI).
Exceptional stakeholder communication and advisory skills, with the ability to influence and guide senior executives.
Recognition as a thought leader or "go-to" expert in data and applied science, both internally and externally.
Expertise in designing scalable end-to-end data pipelines and ETL/ELT architectures
Strong expertise in enterprise data modelling, orchestration, and system integration
Advanced proficiency in Python and SQL for large-scale data engineering
Hands-on experience with PySpark, Databricks, and distributed data processing
Expertise in workflow orchestration using Airflow and similar platforms
Strong experience with SAP HANA and enterprise-grade data platforms
Proficiency in lakehouse and columnar storage technologies such as Parquet and Delta Lake
Experience implementing real-time streaming architectures using Kafka or equivalent technologies
Strong focus on data quality, governance, compliance, and PII management
Expertise in applying CI/CD, Docker, and Git within modern data engineering workflows