At Julius Baer, we celebrate and value the individual qualities you bring, enabling you to be impactful, to be entrepreneurial, to be empowered, and to create value beyond wealth. Let’s shape the future of wealth management together.
Julius Baer is looking for a forward-thinking Data scientist to shape the future of Julius Baer`s enterprise data science workbench offering.In this pivotal role, you will define and drive the technical vision, roadmap, community of practise, and operational excellence of the data science workbench offering of the Global Data Platform enabling data-driven decision-making and reporting across the bank.
You will play a central role in transforming how we build, scale, and govern our
foundational data science infrastructure—balancing innovation with robustness, agility with compliance, and speed with sustainability. Working closely with platform owner, enterprise architecture, product owner, engineering teams, and external partners, you will ensure that the data science workbench capabilities consistently meet evolving business and consumer demands and adhere to stringent IT, risk, and regulatory standards.
PRINCIPALES FUNCIONES
Define and maintain the end-to-end technical vision, roadmap, and lifecycle strategy for the data science workbench offering of the global data platform
Gathering requirements and maintaining relationships with current and potential consumer base. Ensuring the technical vision and roadmap enables their current and future business ambitions
Ensure platform scalability, reliability, performance, and security, embedding non-functional requirements (NFRs), observability, automation, and resilience-by-design principles
Lead the prioritisation and delivery of data science workbench related features and capabilities, collaborating with Platform Owner, Scrum Masters, Development Teams, System Architect, and Release Train Engineer to coordinate work across Agile teams
Own and drive the community of practices for the data science workbench across the organisation
Serve as the primary technical escalation point for incidents, problems, audits, and risk findings related to the data science workbench
Establish and monitor SLAs, SLOs, and KPIs; leverage metrics and OKRs to measure platform health, usage, team performance, and business value delivered
Drive standardisation, reuse, and simplification across the data science workbench ecosystem to reduce complexity, improve interoperability, and accelerate time-to-market
Shape the target operating model for data science workbench operations-including governance, RACI definitions, incident triage, and handover processes-supporting stability, sustainable growth and efficient run-models
YOUR PROFILE
Bachelor's or master’s degree in computer science, Information Systems, or a related field
Minimum of 5 years of experience in technology and/or data science roles within large, regulated enterprises—preferably in financial services
Proven experience designing and operationalising artificial intelligence (AI) and machine learning (ML) platforms, MLOps pipelines, and model lifecycle management frameworks
Hands-on expertise in core data science programming languages (e.g., Python, R, SQL) and libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Experience with Large Language Model (LLM) integration, generative AI frameworks, and prompt engineering within enterprise environments
Strong background in statistical modelling, predictive analytics, and natural language processing (NLP)
Demonstrated experience implementing responsible AI practices, including model validation, bias detection, and explainability in a regulated context
Track record of enabling data science teams by building scalable, governed environments for model development, training, and deployment
Experience with feature stores, vector databases, and AI orchestration tools to support advanced analytics use cases
Ability to translate complex data science and AI requirements into robust, production-grade infrastructure specifications
Proven experience leading and scaling a community of practise
Track record with defining and delivering technical visions and roadmaps in enterprise environments
Hands-on exposure to modern datalake and datalake-house technologies such as Apache Spark / Iceberg, Trino, Kafka, Kubernetes, Snowflake/Databricks, Hadoop ecosystems, or equivalent
Strong understanding of Agile methodologies, DevOps practices, CI/CD pipelines
Familiarity with risk and compliance frameworks applicable to Swiss financial institutions and experience addressing audit points or IT risk items
Exceptional technical leadership and systems thinking, able to balance short-term delivery with long-term platform sustainability
Good communication skills on all levels. Ability to summarise complex issues for management and business, while also being able to discuss on a detail level with technical teams
Expertise in software lifecycle governance, technical debt management, vulnerability remediation, and release approval processes
Skilled in road-mapping, dependency management, and translating business capabilities into actionable technical initiatives
People person or comfortable with significant business / consumer interaction
Solid understanding of non-functional requirements—performance, availability, recoverability, monitoring, logging, and security-by-default
Strong analytical mindset with the ability to make data-informed decisions using KPIs, telemetry, and feedback loops
Fluent in English, both written and verbal; German language skills are an advantage
Ability to operate effectively in a matrixed, multinational environment with competing priorities
We are looking forward to receiving your full job application through our online application tool. Further interesting job opportunities can be found on our Career site.
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