Digital Customer Engagement AI Data Scientist

Siemens Healthineers

About The Position

Join us in pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. Our inspiring and caring environment forms a global community that celebrates diversity and individuality. We encourage you to step beyond your comfort zone, offering resources and flexibility to foster your professional and personal growth, all while valuing your unique contributions. The IT Data Architect designs, creates, deploys and manages an organization's data architecture. Provides a standard common business vocabulary, expresses strategic data requirements, outlines high level integrated designs to meet these requirements, and aligns with enterprise strategy and related business architecture.

Requirements

Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, AI, or a related field.
Strong hands‑on experience with Python and common data science libraries (e.g., pandas, NumPy, scikit‑learn, PyTorch/TensorFlow).
Solid understanding of machine learning algorithms, model evaluation, and statistical methods.
Experience working with SQL and large‑scale datasets.
Practical experience deploying models in production or near‑production environments.
A successful candidate must be able to work with controlled technology in accordance with US export control law.

Nice To Haves

Experience with cloud‑based data & AI platforms (e.g., Azure Machine Learning, Databricks, Snowflake).
Familiarity with Generative AI / LLM use cases, prompt engineering, or vector databases.
Exposure to enterprise data governance and regulated environments.
Experience collaborating in cross‑functional, global teams.
Previous work in highly regulated environments specific to healthcare and healthcare medical technology.

Responsibilities

Design, build, and deploy machine learning and AI models (predictive, prescriptive, and generative) to solve real business problems.
Apply advanced statistical, ML, and deep learning techniques to structured and unstructured data.
Develop and maintain end‑to‑end data science pipelines, from data ingestion and feature engineering to model training and evaluation.
Collaborate with data engineering teams to ensure scalable, reliable, and well‑governed data foundations.
Operationalize AI solutions using MLOps best practices (model versioning, monitoring, drift detection, retraining).
Ensure AI models meet enterprise requirements for performance, robustness, security, and maintainability.
Support cloud‑based AI platforms (e.g., Azure‑based ML environments) used across the organization.
Embed ethical AI principles, transparency, and governance into model design and deployment.
Ensure compliance with internal AI governance frameworks and applicable regulations (e.g., data privacy, AI risk management).
Act as a trusted advisor on responsible AI usage for stakeholders.
Translate business needs into data science problem statements and measurable success criteria.
Communicate complex analytical results clearly to non‑technical audiences.
Partner with product owners, process owners, and IT leaders to drive adoption and value realization.
Develop strategies for data acquisitions, archive recovery, and implementation of a database.
Clean and maintain the database by removing and deleting old data with the help of the team(s).
Define how data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or processing that data in some way.
Set data architecture principles, create models of data that enable the implementation of the intended business architecture, create diagrams showing key data entities, and create an inventory of the data needed to implement the architecture vision.
Ensure compliance to Q, ISEC and DP regulations.