Data Scientist

Siemens Mobility; Raleigh (NC); Remote; USD 86,000 - 148,000; Full time

Job summary

Siemens Mobility is seeking a hands-on Data Scientist to transform asset, maintenance, operational, and product data into production-ready analytical and machine learning solutions.

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.

Qualifications

• 4-5 years of professional experience in data science or ML

Responsibilities

• Translate business problems into analytical questions, hypotheses, and measurable success criteria.
• Prepare data, engineer features, and build/validate models for production use cases.
• Develop and evaluate statistical and ML models for forecasting, anomaly detection, and predictive maintenance.
• Collaborate with product, engineering, and domain teams to deploy and monitor models.
• Own data science workstreams ensuring explainability and measurable impact.

Skills

Python

SQL

Statistics

Time-series

Data wrangling

Model evaluation

Education

Bachelor's or Master's in CS/Stats/DS/ML/OR/Engineering

Tools

Azure

AWS

Databricks

Git

Job description

• Raleigh - North Carolina - United States of America

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.

• 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, assumptions, experiments, model behavior, limitations, and intended use.


• Follow established requirements for data classification, privacy, security, access control, model governance, and responsible AI.


• Contribute to team standards, reusable analytical components, and knowledge sharing.


• Provide technical guidance or informal mentoring to less-experienced team members when appropriate.

This Is How You'll Win Us Over

Required Qualifications

• 4-5 years of professional experience in data science, applied machine learning, predictive analytics, or a closely related field.


• Bachelor's or Master's degree in Computer Science, Statistics, Data Science, Machine Learning, Operations Research, Applied Mathematics, Engineering, or a related quantitative discipline.


• Demonstrated experience delivering data science or machine learning solutions beyond exploratory prototypes, including at least one solution deployed to a product, operational process, or recurring business workflow.

Technical Qualifications

• Strong proficiency in Python and relevant libraries such as pandas, NumPy, scikit-learn, XGBoost, PyTorch, or TensorFlow.


• Strong working proficiency in SQL, including joins, aggregations, window functions, and analysis of production-scale datasets.


• Applied knowledge of statistics, probability, hypothesis testing, feature engineering, model validation, performance measurement, and interpretability.


• Experience with supervised and unsupervised machine learning methods including regression, classification, clustering, tree-based models, anomaly detection, and forecasting.


• Experience preparing and validating imperfect real-world data, including missing values, inconsistent definitions, outliers, leakage risks, and data quality issues.


• Familiarity with Git, code review, testing, documentation, and reproducible development practices.


• Experience using at least one cloud or enterprise data platform such as Azure, AWS, GCP, Snowflake, Databricks, or a comparable environment.


• Familiarity with model deployment or MLOps concepts such as model registries, experiment tracking, batch or API inference, monitoring, and retraining workflows.


• Ability to communicate technical results clearly and connect analytical work to customer, product, or operational outcomes.


• Ability to work effectively across product, engineering, domain, and business teams.

You’ll Thrive Even More If You Also Bring

• Experience with asset management, predictive maintenance, reliability, industrial IoT, facilities, building systems, manufacturing, or another asset-intensive domain.


• Experience with time-series forecasting, survival or reliability analysis, anomaly detection, sensor data, or asset-condition modeling.


• Hands‑on experience with LLM or GenAI use cases such as retrieval-augmented generation (RAG), document extraction, semantic search, prompt evaluation, or structured outputs.


• Familiarity with MLflow, Azure Machine Learning, SageMaker, Vertex AI, Databricks, Airflow, Prefect, dbt, or comparable tools.


• Experience developing explainable models for operational or customer‑facing decisions.


• Familiarity with data governance, data lineage, privacy, model risk management, or responsible AI controls.


• Exposure to simulation, digital twins, Bayesian methods, geospatial data, or optimization techniques.


• Experience mentoring junior practitioners or leading a defined technical workstream.

Our Commitment to Equity and Inclusion in our Diverse Global Workforce: We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the every day with us.

Siemens maintains a Drug Free workplace in accordance with applicable law.

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You’ll Benefit From

Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html The pay range for this position is $86,485 - $148,261 annually. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications, and premium geographic location.

Equal Employment Opportunity Statement

Siemens is an Equal Opportunity Employer encouraging inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law.

EEO is the Law

Applicants and employees are protected from discrimination on the basis of race, color, religion, sex, national origin, or any characteristic protected by Federal or other applicable law.

Reasonable Accommodations

If you require a reasonable accommodation in completing a job application, interviewing, completing any pre‑employment testing, or otherwise participating in the employee selection process, please fill out the accommodations form by clicking on this link Accommodationfor disability form . If you’re unable to complete the form, you can reach out to our AskHR team for support at 1-866-743-6367. Please note our AskHR representatives do not have visibility of application or interview status.

Application Deadline

This role will be posted for a minimum of five (5) days from the original posting date. The posting timeline may change based on applicant volume to ensure we attract and consider a strong and representative candidate pool.

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Pay Transparency

Siemens follows Pay Transparency laws.

Criminal History

Qualified applications with arrest or conviction records will be considered for employment in accordance with applicable local and state laws.