Schreiber Foods
Quality Systems Data Scientist
The Quality Systems Data Scientist will support the design and continuous improvement of Schreiber's global quality management systems (QMS) through development, management and interpretation of Key Performing Indicators (KPIs) and Metrics for Enterprise Quality and Food Safety. This role involves enhancing the QMS through continuous improvement techniques, integrated systems, and predictive analytics to reduce risks and boost efficiency. Collaborating with the Enterprise Quality and Food Safety team and business stakeholders, the Data Scientist will analyze large data sets, identify trends, and develop predictive models. Close collaboration with the Agile Fusion and Data Science Teams is essential to create data-driven solutions that meet business needs and support organizational analytics strategies. This position is located at our home office in Green Bay, Wisconsin. We offer an option to work remotely up to 2 days a week, with at least 3 days being onsite. What you'll do: Business Analysis & Solution Development: Partner with key business stakeholders to identify challenges and opportunities. Integrate business information and data into models and tools to support solution development. Learn and apply Schreiber business systems and Quality Systems processes to drive continuous improvement. Data Science & Machine Learning: Research and develop machine learning applications using structured and unstructured data (e.g., regression, classification, clustering). Apply statistical techniques and machine learning algorithms to solve complex business problems and optimize decision-making. Develop, test, and validate custom models, algorithms, and simulations using Python, R, and SQL. Data Engineering & Integration: Collaborate with Solutions Architects and Data Engineers to integrate Informatica-based ETL processes into Azure Data Factory. Transform and cleanse data using Databricks to ensure accuracy and quality within the data lake. Conduct data mining and algorithm development to optimize processes. Analytics & Visualization: Conduct data analysis to uncover trends and insights. Develop predictive and prescriptive models on Azure's Databricks platform. Present findings using data visualization tools such as Microsoft Power BI to support stakeholder decision-making. Quality Systems & Governance: Define Quality Health Metrics for key QMS processes and systems. Support Quality Systems compliance and monitor global KPIs. Facilitate feedback loops to ensure QMS meets partner needs and lead continuous improvement initiatives. Support QMSDS documentation and training of internal partners. Execute and develop validation test scripts. Collaboration & Project Management: Work collaboratively with Agile Fusion and Data Science teams to support data analytics strategy. Manage multiple projects in a fast-paced, cross-functional environment. Collaborate across multiple time zones to ensure effective communication. Act as a change agent, facilitating onboarding of new systems and updates to existing ones. Innovation & Continuous Learning: Stay current with advancements in data science, machine learning, and predictive analytics. Proactively suggest innovative solutions to enhance data science capabilities. What you need to succeed: Bachelors degree in Data Science, Computer Science, Economics, Mathematics, Statistics or related field. 1+ year of experience in Data Science or related analytics role. 2+ years of experience in Quality systems, Quality Assurance or Quality Engineering experience preferred. 2+ years of experience in Information Technology, or Manufacturing. Proficiency in Python, SQL, Power BI, Databricks, and machine learning techniques (e.g., clustering, classification, regression). Strong foundation in data mining, algorithm development, statistical analysis, and performance testing. Experience with cloud-based data platforms, preferably Microsoft Azure Data Factory and Databricks. Solid understanding of data modeling, predictive analytics, and quality system metrics. Familiarity with data engineering and architectural design principles. Knowledge of Quality System processes, including Non-Conforming Events, CAPA, Training, Document Control, and Audits. Ability to translate complex data into clear, actionable insights for diverse stakeholders. Excellent communication and presentation skills, with experience working across global teams and time zones. Strong organizational and time management skills; able to manage multiple priorities in a fast-paced environment. Demonstrated analytical thinking, problem-solving ability, and a user-centric approach to solution development. Collaborative mindset with the ability to work independently and in cross-functional teams. Self-motivated, adaptable, and open to feedback; committed to continuous learning and improvement. Proven ability to lead by example, drive global standardization, and act as an effective change agent. Experience in program management and understanding of regulatory compliance is a plus. Ability to travel up to 15%.
The Quality Systems Data Scientist will support the design and continuous improvement of Schreiber's global quality management systems (QMS) through development, management and interpretation of Key Performing Indicators (KPIs) and Metrics for Enterprise Quality and Food Safety. This role involves enhancing the QMS through continuous improvement techniques, integrated systems, and predictive analytics to reduce risks and boost efficiency. Collaborating with the Enterprise Quality and Food Safety team and business stakeholders, the Data Scientist will analyze large data sets, identify trends, and develop predictive models. Close collaboration with the Agile Fusion and Data Science Teams is essential to create data-driven solutions that meet business needs and support organizational analytics strategies. This position is located at our home office in Green Bay, Wisconsin. We offer an option to work remotely up to 2 days a week, with at least 3 days being onsite. What you'll do: Business Analysis & Solution Development: Partner with key business stakeholders to identify challenges and opportunities. Integrate business information and data into models and tools to support solution development. Learn and apply Schreiber business systems and Quality Systems processes to drive continuous improvement. Data Science & Machine Learning: Research and develop machine learning applications using structured and unstructured data (e.g., regression, classification, clustering). Apply statistical techniques and machine learning algorithms to solve complex business problems and optimize decision-making. Develop, test, and validate custom models, algorithms, and simulations using Python, R, and SQL. Data Engineering & Integration: Collaborate with Solutions Architects and Data Engineers to integrate Informatica-based ETL processes into Azure Data Factory. Transform and cleanse data using Databricks to ensure accuracy and quality within the data lake. Conduct data mining and algorithm development to optimize processes. Analytics & Visualization: Conduct data analysis to uncover trends and insights. Develop predictive and prescriptive models on Azure's Databricks platform. Present findings using data visualization tools such as Microsoft Power BI to support stakeholder decision-making. Quality Systems & Governance: Define Quality Health Metrics for key QMS processes and systems. Support Quality Systems compliance and monitor global KPIs. Facilitate feedback loops to ensure QMS meets partner needs and lead continuous improvement initiatives. Support QMSDS documentation and training of internal partners. Execute and develop validation test scripts. Collaboration & Project Management: Work collaboratively with Agile Fusion and Data Science teams to support data analytics strategy. Manage multiple projects in a fast-paced, cross-functional environment. Collaborate across multiple time zones to ensure effective communication. Act as a change agent, facilitating onboarding of new systems and updates to existing ones. Innovation & Continuous Learning: Stay current with advancements in data science, machine learning, and predictive analytics. Proactively suggest innovative solutions to enhance data science capabilities. What you need to succeed: Bachelors degree in Data Science, Computer Science, Economics, Mathematics, Statistics or related field. 1+ year of experience in Data Science or related analytics role. 2+ years of experience in Quality systems, Quality Assurance or Quality Engineering experience preferred. 2+ years of experience in Information Technology, or Manufacturing. Proficiency in Python, SQL, Power BI, Databricks, and machine learning techniques (e.g., clustering, classification, regression). Strong foundation in data mining, algorithm development, statistical analysis, and performance testing. Experience with cloud-based data platforms, preferably Microsoft Azure Data Factory and Databricks. Solid understanding of data modeling, predictive analytics, and quality system metrics. Familiarity with data engineering and architectural design principles. Knowledge of Quality System processes, including Non-Conforming Events, CAPA, Training, Document Control, and Audits. Ability to translate complex data into clear, actionable insights for diverse stakeholders. Excellent communication and presentation skills, with experience working across global teams and time zones. Strong organizational and time management skills; able to manage multiple priorities in a fast-paced environment. Demonstrated analytical thinking, problem-solving ability, and a user-centric approach to solution development. Collaborative mindset with the ability to work independently and in cross-functional teams. Self-motivated, adaptable, and open to feedback; committed to continuous learning and improvement. Proven ability to lead by example, drive global standardization, and act as an effective change agent. Experience in program management and understanding of regulatory compliance is a plus. Ability to travel up to 15%.