Senior Staff Data Scientist Neurotechnology

This is a hybrid 18 month fixed term maternity leave contract role based in Carrigtwohill Cork.

This role does not meet the criteria to allow Stryker to sponsor a Critical Skills work permit. Candidates must be resident in the Republic of Ireland and eligible to work for the full duration of the contract

Want to use your unique skillset and talent to transform lives Join us as a Staff Engineer Data Science Neurotechnology and help advance neurosurgery by translating complex data into insights that shape surgical decision making and smarter product innovation.
You will work closely with technical and business leads representing a diverse and robust array of surgical instrumentation including imaging visualization navigation robotics and various powered smart instrumentation. You will partner closely with a global R&D team multi-disciplined OR clinical teams and marketing as we collectively advance neurosurgery and transform the lives of patients.
You will:
Provide hands on data engineering to extract valuable insights from Strykers connected Neurotechnology ecosystem enabling data informed clinical performance product design and operational support.
Design and implement scalable production grade data pipelines to support both clinical and operational use cases.
Optimize data ingestion processing reliability and performance to meet the evolving digital demands of the future Neuro Operating Room.
Develop and validate proof-of-concepts and prototype applications to assess feasibility demonstrate value and directly inform the Neurotechnology data strategy.
Partner with data cloud and front-end application engineers to evolve prototypes into reliable enterprise-ready solutions.
Work within Python based data science workflows and enterprise platforms on the Stryker Health Cloud collaborating closely with advanced R&D marketing and external stakeholders.
Write clean efficient and well documented Python code for data modelling data processing feature engineering analytics and visualization supporting next generation neurotechnology products.
Collaborate across disciplines to deliver actionable data-driven insights that drive clinical innovation and operational excellence.

Skills needed:
Strong proficiency in Python for data collection analysis and data pipeline architecture with an emphasis on scalability.

Proficiency with data visualization standards and tools with a focus on clarity usability and insight generation.
Ability to ideate and identify evaluate and integrate novel data sources and explore their potential value in developing clinical insights
Solid foundation in software engineering electrical engineering and/or systems engineering fundamentals enabling effective collaboration across technical disciplines.
Broad practical experience across the data lifecycle including strong analytical and problem solving skills with experience translating complex data into meaningful real world impact.
Flexible agile mindset with a systems level design approach balancing rapid prototyping with long term scalability reliability and organizational integration.
Bonus: Experience implementing end-to-end AI/ML pipelines from data ingestion and preprocessing to model training evaluation and CI / CD.
Bonus: Hands-on experience with machine learning algorithms and predictive modeling in applied contexts.
Bonus: exposure to deploying optimized real-time inference models on edge compute systems

Requirements:
Bachelors degree in Computer Science Software Engineering Electrical Engineering or a related technical discipline
Experience developing data solutions in regulated or clinical environments preferred
3-5 years of experience delivering solutions in a data architecture data engineering or related field
Demonstrated experience designing and implementing scalable data models (relational dimensional NoSQL) and data warehouse systems
Hands-on experience with modern cloud data platforms (e.g. Databricks) and data governance lineage and compliance tooling
Proven ability to excel in crossfunctional collaboration with clinical partners R&D IT and business stakeholders to translate clinical needs into robust scalable data platform designs