Role Description The Data Modernization Senior Advisor will serve as a subject matter expert, guiding public health agencies through the development and delivery of technical projects that improve the use of public health data to inform decision making. This role is aligned to the Workforce Acceleration Initiative (WAI), a federally funded CDC Foundation program aimed at helping the nation’s public health agencies. The advisor will support GPTEC’s ongoing development and expansion of Winter Count, a tribally owned cloud-based public health data ecosystem. This role will guide the strategic evolution of the Winter Count ecosystem through: - Expanded data integrations - Enhanced analytics capabilities - Improvements to system usability - Strengthened governance structures that support Tribal data sovereignty The Data Modernization Senior Advisor will develop and implement the organization’s strategy for effective use of data and information through improvements in: - Information systems - Policy - Workforce - Work processes This position will strategically direct the organization’s data modernization effort, identifying and addressing needs, gaps, and opportunities in the organization’s data ecosystem. This position is eligible for a fully remote work arrangement for U.S. based candidates. Responsibilities - Collaborate with GPTEC leadership and Tribal Public Health Authorities to assess data needs and identify opportunities to strengthen the Winter Count data ecosystem. - Lead the development and implementation of GPTEC’s long-term data modernization strategy. - Provide strategic guidance on the continued development and expansion of the Winter Count data hub. - Support the enhancement and deployment of the Electronic Disease Surveillance System (EDSS). - Advise on the integration of Tribal, state, federal, and programmatic data sources. - Guide the development and implementation of data governance structures and policies. - Lead efforts to modernize GPTEC’s data infrastructure. - Assess and modernize GPTEC’s data visualization and analytics environment. - Conduct organizational data and system assessments. - Support Tribal needs assessments and stakeholder engagement. - Provide strategic guidance on change management and workforce readiness. - Support development of training resources and technical documentation. - Advise GPTEC leadership on sustainability planning. - Facilitate coordination among GPTEC leadership, WAI placements, and partner organizations. - Participate in project meetings, milestone reviews, and reporting activities. - Perform other related duties as assigned. - Up to 10% domestic travel may be required. Qualifications - Bachelor's degree in public health, computer information systems, health informatics, epidemiology or related field; Master’s degree preferred. - Minimum of 10 years of relevant professional experience in epidemiology, informatics, data science, or information systems development. - Minimum of 5 years of experience leading or collaborating on technical data strategies. - Demonstrated experience supporting the development or implementation of public health data ecosystems. - Knowledge of modern data architectures, including cloud-based infrastructure. - Experience working with public health agencies or Tribal organizations. - Understanding of data governance and policies supporting responsible data sharing. - Familiarity with national public health data modernization efforts. - Understanding of information systems development and data life cycles. - Strong project management skills and ability to meet deadlines. - Ability to engage with a broad range of stakeholders. - Strong written and verbal communication skills. - Superior organizational skills and attention to detail. - Excellent interpersonal skills and ability to represent the organization. - Creativity and problem-solving skills. Job Highlights - Location: Remote, must be based in the United States. - Salary Range: $135,000 - $166,050, per year, plus benefits. - Position Type: Grant funded, limited-term opportunity. - Position End Date: June 30, 2027. Special Notes This role is involved in a dynamic public health program. As such, roles and responsibilities are subject to change as situations evolve. All qualified applicants will receive consideration for employment without discrimination.

ZADANIA: • Realizacja projektów logistycznych • Udział w warsztatach montażowych i logistycznych • Analiza, ocena i wdrożenie środków optymalizacji istotnych dla logistyki • Osoba kontaktowa dla klientów zewnętrznych, dostawców i działów wewnętrznych VW • Kontrola kosztów ogólnych projektu i materiałów, śledzenie budżetu i śledzenie kosztów w ramach projektów • Kalkulacja usług logistycznych (JIT/ JIS, FTS, planowanie pojemników, symulacja procesów) • Planowanie i koordynacja projektów infrastrukturalnych i logistycznych • Wyjazdy służbowe – 2-3 razy w miesiącu do Niemiec OCZEKIWANIA: • Wykształcenie wyższe preferowane (studia z zakresu logistyki, ekonomii, inżynierii lub innych technicznych kierunków) • Doświadczenie zawodowe w środowisku planowania logistycznego w branży automotive • Bardzo dobra znajomość języka niemieckiego (min. B2) – warunek konieczny • Znajomość języka angielskiego w stopniu komunikatywnym – mile widziane • Znajomość Microsoft Office • Dogłębna znajomość planowania przepływu materiałów, symulacji procesów/ EAWS lub REFA • Obsługa systemów takich jak Arbeitsplan, Microstation / HLS, CATIA, Legato, KVS • Analityczne podejście w innowacyjnym środowisku • Orientacja na klienta • Umiejętność pracy w zespole i samodzielnie • Prosimy o przysyłanie CV w j. niemieckim/angielskim OFERUJEMY: • Możliwości rozwoju w międzynarodowej organizacji, • Kulturę organizacyjną opartą na szacunku i zaufaniu, • Szeroki i elastyczny pakiet benefitów, • Możliwość pracy w 100% zdalnej.

Brief Role Description ZADANIA: • Realizacja projektów logistycznych • Udział w warsztatach montażowych i logistycznych • Analiza, ocena i wdrożenie środków optymalizacji istotnych dla logistyki • Osoba kontaktowa dla klientów zewnętrznych, dostawców i działów wewnętrznych VW • Kontrola kosztów ogólnych projektu i materiałów, śledzenie budżetu i śledzenie kosztów w ramach projektów • Kalkulacja usług logistycznych (JIT/ JIS, FTS, planowanie pojemników, symulacja procesów) • Planowanie i koordynacja projektów infrastrukturalnych i logistycznych • Wyjazdy służbowe – 2-3 razy w miesiącu do Niemiec OCZEKIWANIA: • Wykształcenie wyższe preferowane (studia z zakresu logistyki, ekonomii, inżynierii lub innych technicznych kierunków) • Doświadczenie zawodowe w środowisku planowania logistycznego w branży automotive • Bardzo dobra znajomość języka niemieckiego (min. B2) – warunek konieczny • Znajomość języka angielskiego w stopniu komunikatywnym – mile widziane • Znajomość Microsoft Office • Dogłębna znajomość planowania przepływu materiałów, symulacji procesów/ EAWS lub REFA • Obsługa systemów takich jak Arbeitsplan, Microstation / HLS, CATIA, Legato, KVS • Analityczne podejście w innowacyjnym środowisku • Orientacja na klienta • Umiejętność pracy w zespole i samodzielnie • Prosimy o przysyłanie CV w j. niemieckim/angielskim OFERUJEMY: • Możliwości rozwoju w międzynarodowej organizacji, • Kulturę organizacyjną opartą na szacunku i zaufaniu, • Szeroki i elastyczny pakiet benefitów, • Możliwość pracy w 100% zdalnej.

Role Description This role is responsible for developing industrialized optimisation and machine learning models as part of a full-stack product squad that delivers operations decision-support software. As a key member of a product squad and reporting to the Lead Product Data Scientist, a Data Scientist will: - Develop data pipelines, machine learning models, and complex optimization models in the ODS software product suite. - Oversee modelling and robust implementation of features contributing to an operations decision-support product. - Ensure that features integrate seamlessly into the product’s technical stack (data ingestion, user interface, orchestration) as well as the business process and use case. Accountabilities - Understand a business problem and its component processes end to end, identifying opportunities to make decisions more optimally leveraging decision-support tooling. - Conduct analyses and visualizations to identify valuable opportunities for decision-support and determine trade-offs between different potential feature implementations. - Prototype advanced machine learning and optimization models to prove the value of a use case and approach (in Python). - Deliver features to industrialize machine learning and optimization models in Python using best-practice software principles. - Build automated, robust data cleaning pipelines that follow software best-practices (in Python). - Implement integrations between the core algorithm (machine-learning or optimization) and a workflow orchestration paradigm such as Dagster. - Implement software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles. - Build logging, error handling, and automated tests (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products. - Deliver features to harden an algorithm against edge cases in the operation and in data. - Conduct analysis to quantify the adoption and value-capture from a decision-support product. - Engage with business stakeholders to collect requirements and get feedback. - Contribute to conversations on feature prioritisation and roadmap, understanding the trade-off between speed vs. long-term value. - Understand and integrate the product into existing business processes, contributing to the development and adoption of new business processes leveraging a decision-support product. - Communicate feature and modeling approach, trade-offs, and results with the internal team and business stakeholders. Skills/capabilities - Strong knowledge of machine learning and optimization techniques, including supervised (regression, tree methods, etc.), unsupervised (clustering) learning, and operations research (linear, mixed integer programming, heuristics). - Fluent in Python (required) and other programming languages (preferred) with strong skills in applying DS, ML, and OR packages (scikit-learn, pandas, numpy, gurobi, etc.) to solve real-life problems and visualize the outcomes (e.g., seaborn). - Proficient in working with cloud platforms (AWS preferred), code versioning (Git), and experiment tracking (e.g., MLflow). - Experience with cloud-based ML tools (e.g., SageMaker), data and model versioning (e.g., DVC), CI/CD (e.g., GitHub Actions), workflow orchestration (e.g., Airflow/Dagster), and containerized solutions (e.g., Docker, ECS) is nice to have. - Experience in code testing (unit, integration, end-to-end tests). - Strong data engineering skills in SQL and Python. - Proficient in the use of Microsoft Office, including advanced Excel and PowerPoint skills. - Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights. - Understanding of the trade-offs of different data science, machine learning, and optimization approaches, and ability to intelligently select the best candidates to solve a particular business problem. - Able to structure business and technical problems, identify trade-offs, and propose solutions. - Communication of advanced technical concepts to audiences with varying levels of technical skills. - Managing priorities and timelines to deliver features in a timely manner that meet business requirements. - Collaborative team-working, giving and receiving feedback, and always seeking to improve team processes. Qualifications - Master’s degree or greater in data science, ML, or operational research, or 2+ years of highly relevant industry experience (required). - 0-2 years working on production ML or optimization software products at scale (required). - Experience in developing industrialized software, especially data science or machine learning software products (preferred). - Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred). Key interfaces - Lead Product Data Scientist - Other Data Scientists - Business stakeholders and users - Software engineers (front-end, back-end, DevOps, data engineers) - Product & change managers - BA Digital teams (e.g., architects, application support managers) - External partners and third parties, as required - ODS Leadership (Head of Data & Analytics, Head of iOps & Optimisation, Director of ODS) Key performance indicators - Model accuracy, performance, and runtime (precision, recall, accuracy) - Time to develop and deploy features and models - Data ingestion & processing efficiency and robustness - Code quality and robustness (e.g., unit test coverage) - Collaboration and cross-functional teamwork Behaviours and attitude - I’m a role model for all BA brand behaviours and ways of working – I walk the talk. - I exude a can-do attitude (best of BA). - I’m flexible and agile, always ready to adapt when things don’t go to plan. - I’m an ambassador for BA and my team. - I role model our Leadership Behaviours. Core traits - Systems thinking - Detail oriented while understanding the big picture - Curious, self-motivated, proactive, and action-oriented - Creative and innovative - Resilient and flexible in light of changing priorities and approaches - Data-driven - Pragmatic - Collaborative - A true believer in the power of using data to drive better decision making - A technologist, interested in keeping up with the latest and greatest in software development, optimization, and machine learning - Commitment to delivering business value Interview Questions Ask the candidate the following questions and share the summary along with the candidate resume: - Tell me about an optimisation problem you have solved? - What was the business problem and the context of it? - Explain what the measure was? - What were the constraints? - Which algorithm did you use to solve this? - What was your end result?