Data Scientist

Siemens•Raleigh, NC

1d•$86,485 - $148,261•Remote

About The Position

Siemens is seeking a hands-on Data Scientist to turn asset, maintenance, operational, and product data into production-ready analytical and machine learning solutions. The role partners with product managers, engineers, domain experts, and customer-facing teams to frame problems, prepare data, develop and evaluate models, and support deployment into production workflows. The Data Scientist independently owns defined workstreams and is accountable for the quality, explainability, and measurable impact of their work. The position is remote within the United States after an initial onsite onboarding week in Raleigh, North Carolina. Applicants must be legally authorized to work in the United States without current or future employer-sponsored work authorization. The role focuses on applied data science and machine learning, experimentation and business impact, productionization, emerging GenAI capabilities, collaboration, and responsible AI. Siemens describes a culture of innovation, collaboration, and continuous learning, and offers health and wellness benefits. The annual pay range is $86,485–$148,261, with the actual offer dependent on factors including experience, skills, qualifications, budget, and geographic location.

Requirements

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.
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 handling 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.
Legally authorized to work in the United States without requiring employer-sponsored work authorization now or in the future.
Attend the first week of employment onsite in Raleigh, North Carolina for onboarding.

Nice To Haves

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 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.

Responsibilities

Translate customer, product, and operational problems into 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 forecasting, anomaly detection, asset condition assessment, failure prediction, predictive maintenance, and decision support.
Select baselines, features, algorithms, validation strategies, and performance metrics suited to business objectives and data characteristics.
Perform error analysis, sensitivity analysis, and model interpretation; develop explainable outputs for product teams, domain experts, and customers.
Use statistical methods, hypothesis testing, and experimental or quasi-experimental techniques to assess product and model impact.
Define baseline measures and compare model-driven approaches with existing processes or business rules; identify adoption measures, operational KPIs, and business outcomes with stakeholders.
Communicate findings, assumptions, tradeoffs, and recommendations to technical and non-technical audiences, and monitor the value of deployed solutions.
Write maintainable, tested, and documented Python and SQL code; create reproducible data preparation, feature engineering, training, and evaluation workflows.
Collaborate with ML, data, software, and platform engineers to package, deploy, monitor, improve, and troubleshoot production models and data workflows.
Contribute to model documentation, version control, automated testing, code reviews, CI/CD workflows, and monitoring requirements for data quality, performance, drift, reliability, and operational failures.
Evaluate appropriate bounded uses of LLM and GenAI; support prototyping and evaluation of prompt-based, RAG, and structured-output workflows, with suitable evaluation, traceability, privacy, security, and human-review controls.
Compare AI-enabled approaches with simpler statistical, rules-based, or workflow solutions before recommending implementation.
Work with product managers, engineers, UX practitioners, domain experts, and customer-facing teams throughout delivery; participate in technical reviews and provide evidence-based recommendations.
Document data sources, assumptions, experiments, model behavior, limitations, and intended use; follow data classification, privacy, security, access control, model governance, and responsible AI requirements.
Contribute to team standards, reusable analytical components, and knowledge sharing; provide technical guidance or informal mentoring when appropriate.