{"id":2037070,"url":"https://alion.io/job/siemens-data-scientist-2","title":"Data Scientist","company":{"id":131,"name":"Siemens","domain":"siemens.com","url":"https://alion.io/company/siemens","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Raleigh, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":86485,"max":148261,"currency":"USD","period":"year","gross":null,"usd_annual":148261},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Digital Twin","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-07T16:23:38Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-12T20:52:56Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"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.\nDescription:\nWe are looking for a hands-on Data Scientist to transform asset, maintenance, operational, and product data into production-ready analytical and machine learning solutions.\nIn 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.\nYou 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.\nYou'll Make an Impact By:\nApplied Data Science and Machine Learning\nTranslate customer, product, and operational problems into clear analytical questions, hypotheses, modeling approaches, and measurable success criteria.\nExplore, clean, validate, and combine structured, time-series, sensor, work-order, inspection, and unstructured data.\nDevelop 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.\nSelect appropriate baselines, features, algorithms, validation strategies, and performance metrics based on business objectives and data characteristics.\nPerform error analysis, sensitivity analysis, and model interpretation to understand model performance, reliability, and limitations.\nBuild explainable outputs that product teams, domain experts, and customers can understand and act upon.\nExperimentation and Business Impact\nApply statistical methods, hypothesis testing, and experimental or quasi-experimental techniques to evaluate product and model impact.\nDefine baseline measures and compare model-driven approaches against existing processes or business rules.\nPartner with stakeholders to identify adoption measures, operational KPIs, and business outcomes.\nCommunicate findings, assumptions, tradeoffs, and recommendations to both technical and non-technical audiences.\nMonitor whether deployed solutions continue to deliver intended customer and business value.\nProductionization and Engineering CollaborationWrite maintainable, tested, and documented Python and SQL code using established software engineering practices.\nCreate reproducible data preparation, feature engineering, training, and evaluation workflows.\nCollaborate with ML, data, and software engineers to package, deploy, monitor, and improve models in production.\nContribute to model documentation, version control, automated testing, code reviews, and CI/CD workflows.\nHelp define monitoring requirements for data quality, model performance, drift, reliability, and operational failures.\nTroubleshoot model and data issues in partnership with engineering and platform teams.\nGenAI and Emerging AI Capabilities\nEvaluate 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.\nSupport prototyping and evaluation of prompt-based, retrieval-augmented generation (RAG), and structured-output workflows.\nApply appropriate evaluation, traceability, privacy, security, and human-review controls when working with GenAI technologies.\nCompare AI-enabled approaches against simpler statistical, rules-based, or workflow solutions before recommending implementation.\nCollaboration and Responsible AI\nWork closely with product managers, engineers, UX practitioners, domain experts, and customer-facing teams throughout the delivery lifecycle.\nParticipate in technical reviews and provide evidence-based recommendations on modeling choices and implementation tradeoffs.\nDocument data sources, assumptions, experiments, model behavior, limitations, and intended use.\nFollow established requirements for data classification, privacy, security, access control, model governance, and responsible AI.\nContribute to team standards, reusable analytical components, and knowledge sharing.\nProvide technical guidance or informal mentoring to less-experienced team members when appropriate.\nThis Is How You'll Win Us Over\nRequired Qualifications\n4-5 years of professional experience in data science, applied machine learning, predictive analytics, or a closely related field.\nBachelor's or Master's degree in Computer Science, Statistics, Data Science, Machine Learning, Operations Research, Applied Mathematics, Engineering, or a related quantitative discipline.\nDemonstrated 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.\nTechnical Qualifications\nStrong proficiency in Python and relevant libraries such as pandas, NumPy, scikit-learn, XGBoost, PyTorch, or TensorFlow.\nStrong working proficiency in SQL, including joins, aggregations, window functions, and analysis of production-scale datasets.\nApplied knowledge of statistics, probability, hypothesis testing, feature engineering, model validation, performance measurement, and interpretability.\nExperience with supervised and unsupervised machine learning methods including regression, classification, clustering, tree-based models, anomaly detection, and forecasting.\nExperience preparing and validating imperfect real-world data, including missing values, inconsistent definitions, outliers, leakage risks, and data quality issues.\nFamiliarity with Git, code review, testing, documentation, and reproducible development practices.\nExperience using at least one cloud or enterprise data platform such as Azure, AWS, GCP, Snowflake, Databricks, or a comparable environment.\nFamiliarity with model deployment or MLOps concepts such as model registries, experiment tracking, batch or API inference, monitoring, and retraining workflows.\nAbility to communicate technical results clearly and connect analytical work to customer, product, or operational outcomes.\nAbility to work effectively across product, engineering, domain, and business teams.\nYou'll Thrive Even More If You Also Bring\nExperience with asset management, predictive maintenance, reliability, industrial IoT, facilities, building systems, manufacturing, or another asset-intensive domain.\nExperience with time-series forecasting, survival or reliability analysis, anomaly detection, sensor data, or asset-condition modeling.\nHands-on experience with LLM or GenAI use cases such as retrieval-augmented generation (RAG), document extraction, semantic search, prompt evaluation, or structured outputs.\nFamiliarity with MLflow, Azure Machine Learning, SageMaker, Vertex AI, Databricks, Airflow, Prefect, dbt, or comparable tools.\nExperience developing explainable models for operational or customer-facing decisions.\nFamiliarity with data governance, data lineage, privacy, model risk management, or responsible AI controls.\nExposure to simulation, digital twins, Bayesian methods, geospatial data, or optimization techniques.\nExperience mentoring junior practitioners or leading a defined technical workstream.\nAt Siemens, you'll have the opportunity to grow your career while helping organizations operate smarter, safer, and more sustainably. We foster a culture of innovation, collaboration, and continuous learning, where employees are empowered to make a difference every day. If you're excited about solving real-world challenges and shaping the future of asset management, we encourage you to apply.\nOur Commitment to Equity and Inclusion in our Diverse Global Workforce:\nWe 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.\nSiemens maintains a Drug Free workplace in accordance with applicable law.\nYou’ll Benefit FromSiemens 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\nThe 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.\nEqual Employment Opportunity Statement\nSiemens 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.\nEEO is the Law\nApplicants 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.\nReasonable Accommodations\nIf 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 Accommodation\nfor 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.\nPay Transparency\nSiemens follows Pay Transparency laws.\nApplication Deadline\nThis 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.\nCalifornia Privacy Notice\nCalifornia residents have the right to receive additional notices about their\npersonal information. To learn more, click here.\n\nCriminal HistoryQualified applications with arrest or conviction records will be considered for\nemployment in accordance with applicable local and state laws.","description_format":"text","description_chars":11166,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Rail Transport","Medical Imaging Systems","Building Automation & Controls","Manufacturing & Industrial Software"],"lifecycle":[{"event":"open","at":"2026-10-07T16:23:38Z"}],"visa":[],"liveness":{"score":28,"band":"fade","label":"Fading","p_open":1,"p_active":0.617,"p_room":0.45,"age_days":4,"expected_fill_days":3,"reasons":["conf:2","velocity","win:tail","comp:brand"],"computed_at":"2026-10-12T06:55:09Z"},"pay":{"stated_usd_annual":148261,"is_top_pay":true},"html_url":"https://alion.io/job/siemens-data-scientist-2","json_url":"https://alion.io/job/siemens-data-scientist-2.json","meta":{"generated_at":"2026-10-13T00:20:43Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":499,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":131},"rest":"https://alion.io/mcp/rest/get_company?id=131"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsiemens-data-scientist-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsiemens-data-scientist-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsiemens-data-scientist-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/siemens-data-scientist-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsiemens-data-scientist-2"}]}