Data Scientist

At Siemens, we help organizations transform maintenance and operations through connected insights, AI-powered technology, and intelligent asset management solutions. Our software enables customers to manage the full lifecycle of assets, facilities, and infrastructure while improving efficiency, reducing risk, and optimizing long-term investments. By connecting data, people, and processes, we empower organizations to make smarter decisions, maximize asset performance, and achieve more resilient operations.

Description We are looking for a hands-on Data Scientist to transform asset, maintenance, operational, and product data into production-ready analytical and machine learning solutions.

In this role, you will partner with product managers, engineers, domain experts, and customer-facing teams to frame business problems, prepare data, develop and evaluate models, and support deployment into production workflows.

You will independently own well-defined data science workstreams and be accountable for the quality, explainability, and measurable impact of your work. This role is ideal for someone with strong applied statistics, Python, SQL, and machine learning experience who can move effectively from exploration and experimentation to validated, production-ready solutions.

This is a remotely based role in the U.S.

Qualified Applicants must be legally authorized for employment in the United States and will not require employer sponsored work authorization now or in the future for employment in the United States.

You’ll Make an Impact By: Applied Data Science and Machine Learning

Translate customer, product, and operational problems into clear analytical questions, hypotheses, modeling approaches, and measurable success criteria.

Explore, clean, validate, and combine structured, time-series, sensor, work-order, inspection, and unstructured data.

Develop and evaluate statistical and machine learning models for use cases such as forecasting, anomaly detection, asset condition assessment, failure prediction, predictive maintenance, and decision support.

Select appropriate baselines, features, algorithms, validation strategies, and performance metrics based on business objectives and data characteristics.

Perform error analysis, sensitivity analysis, and model interpretation to understand model performance, reliability, and limitations.

Build explainable outputs that product teams, domain experts, and customers can understand and act upon.

Experimentation and Business Impact

Apply statistical methods, hypothesis testing, and experimental or quasi-experimental techniques to evaluate product and model impact.

Define baseline measures and compare model-driven approaches against existing processes or business rules.

Partner with stakeholders to identify adoption measures, operational KPIs, and business outcomes.

Communicate findings, assumptions, tradeoffs, and recommendations to both technical and non-technical audiences.

Monitor whether deployed solutions continue to deliver intended customer and business value.

Productionization and Engineering Collaboration

Write maintainable, tested, and documented Python and SQL code using established software engineering practices.

Create reproducible data preparation, feature engineering, training, and evaluation workflows.

Collaborate with ML, data, and software engineers to package, deploy, monitor, and improve models in production.

Contribute to model documentation, version control, automated testing, code reviews, and CI/CD workflows.

Help define monitoring requirements for data quality, model performance, drift, reliability, and operational failures.

Troubleshoot model and data issues in partnership with engineering and platform teams.

GenAI and Emerging AI Capabilities

Evaluate where LLM and GenAI capabilities may be appropriate for bounded use cases such as document understanding, intelligent search, structured extraction, report generation, and conversational access to data.

Support prototyping and evaluation of prompt-based, retrieval-augmented generation (RAG), and structured-output workflows.

Apply appropriate evaluation, traceability, privacy, security, and human-review controls when working with GenAI technologies.

Compare AI-enabled approaches against simpler statistical, rules-based, or workflow solutions before recommending implementation.

Collaboration and Responsible AI

Work closely with product managers, engineers, UX practitioners, domain experts, and customer-facing teams throughout the delivery lifecycle.

Participate in technical reviews and provide evidence-based recommendations on modeling choices and implementation tradeoffs.

Document data sources, assumpt