Data Scientist 100% (f/m/d)

ppAt 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. Lets shape the future of wealth management together. /p pJulius Baer is looking for a forward-thinking Data scientist to shape the future of Julius Baer`s enterprise data science workbench offering. /p pIn 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. /p pYou 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. /p h3YOUR CHALLENGE /h3 ul liDefine and maintain the end-to-end technical vision, roadmap, and lifecycle strategy for the data science workbench offering of the global data platform /li liGathering requirements and maintaining relationships with current and potential consumer base. Ensuring the technical vision and roadmap enables their current and future business ambitions /li liEnsure platform scalability, reliability, performance, and security, embedding non-functional requirements (NFRs), observability, automation, and resilience-by-design principles /li liLead 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 /li liOwn and drive the community of practices for the data science workbench across the organisation /li liServe as the primary technical escalation point for incidents, problems, audits, and risk findings related to the data science workbench /li liEstablish and monitor SLAs, SLOs, and KPIs; leverage metrics and OKRs to measure platform health, usage, team performance, and business value delivered /li liDrive standardisation, reuse, and simplification across the data science workbench ecosystem to reduce complexity, improve interoperability, and accelerate time-to-market /li liShape 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 /li /ul h3YOUR PROFILE /h3 ul liBachelor's or master's degree in computer science, Information Systems, or a related field /li liMinimum of 5 years of experience in technology and/or data science roles within large, regulated enterprises - preferably in financial services /li liProven experience designing and operationalising artificial intelligence (AI) and machine learning (ML) platforms, MLOps pipelines, and model lifecycle management frameworks /li liHands-on expertise in core data science programming languages (e.g., Python, R, SQL) and libraries (e.g., TensorFlow, PyTorch, Scikit-learn) /li liExperience with Large Language Model (LLM) integration, generative AI frameworks, and prompt engineering within enterprise environments /li liStrong background in statistical modelling, predictive analytics, and natural language processing (NLP) /li liDemonstrated experience implementing responsible AI practices, including model validation, bias detection, and explainability in a regulated context /li liTrack record of enabling data science teams by building scalable, governed environments for model development, training, and deployment /li liExperience with feature stores, vector databases, and AI orchestration tools to support advanced analytics use cases /li liAbility to translate complex data science and AI requirements into robust, production-grade infrastructure specifications /li liProven experience leading and scaling a community of practise /li liTrack record with defining and delivering technical visions and roadmaps in enterprise environments /li liHands-on exposure to modern datalake and datalake-house technologies such as Apache Spark / Iceberg, Trino, Kafka, Kubernetes, Snowflake/Databricks, Hadoop ecosystems, or equivalent /li liStrong understanding of Agile methodologies, DevOps practices, CI/CD pipelines /li liFamiliarity with risk and compliance frameworks applicable to Swiss financial institutions and experience addressing audit points or IT risk items /li liExceptional technical leadership and systems thinking, able to balance short-term delivery with long-term platform sustainability /li liGood 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 /li liExpertise in software lifecycle governance, technical debt management, vulnerability remediation, and release approval processes /li liSkilled in road-mapping, dependency management, and translating business capabilities into actionable technical initiatives /li liPeople person or comfortable with significant business / consumer interaction /li liSolid understanding of non-functional requirements - performance, availability, recoverability, monitoring, logging, and security-by-default /li liStrong analytical mindset with the ability to make data-informed decisions using KPIs, telemetry, and feedback loops /li liFluent in English, both written and verbal; German language skills are an advantage /li liAbility to operate effectively in a matrixed, multinational environment with competing priorities /li /ul /p #J-18808-Ljbffr