DUTIES AND RESPONSIBILITES:
• Responsible for elicitation, analysis, and documentation of business requirements.
• Research and exploration of user requirements, costs, and benefits in support of documentation required for the demand management process.
• Responsible for analyzing the business needs to identify new processes and process improvements that will meet those needs.
• Documents current business processes and models to define requirements and/or gaps.
• Analyze detailed system factors including input/output requirements, information and paper flow, hardware, and software needs.
• Analyze “soft" system factors including roles, responsibilities, policy, culture, etc.
• Evaluate the impact of potential software and process changes on the organization.
• Develop and facilitate groups as necessary in pursuit of eliciting and defining requirements.
• Assist in the creation of the business case documentation.
• Understanding of architectural consistency and usability standards.
• Defines and develops user test cases and validates test results during testing.
• Executes unit and functional test cases.
• May conduct system demonstrations and training sessions, as well as participate in design/review of training content.
• Plays a third level support role by helping to troubleshoot and resolve issues with production systems.
• Participates in associate meetings and communicates any concerns to management.
QUALIFICATIONS AND REQUIREMENTS:
• Bachelor’s degree in computer science, Information Systems, Business, or another related field or equivalent work experience.
• Proven experience leading data migration requirements from legacy systems into a new ERP (Sage X3) environment
• Strong hands-on expertise in requirements elicitation, documentation, and management, with emphasis on data mapping, transformation rules, and validation
• Deep understanding of data structures, data quality, and migration best practices, with the ability to identify gaps and risks early
• Self-driven and accountable professional with strong stakeholder management and communication skills, able to drive outcomes with minimal oversight
• Working knowledge of Confluence and JIRA.
• Experience in any of the following business areas is desired: Sales operations, order entry, accounting, finance, procurement, warehouse operations, order management, and order fulfillment.
• Minimum of 3-5 years’ business analysis or systems analysis experience is preferred.
• Bonus: Sage X3 experience (ERP).
Associate is expected to operate at a proficient level across the following areas of technical knowledge and skill:
• Ability to organize and prioritize work.
• Detail oriented, proactive, creative, and efficient
• Intermediate level abilities in Excel, Outlook, PowerPoint, Visio, and Word
• Familiarity with application integration methods to include understanding of data mapping process.
• Ability to effectively work both independently and collaboratively in a team environment
• Ability to work on simultaneous and complex projects, with domain expertise in at least one business area.
• Expertise in ERP solution selection and implementation is a plus
• Ability to communicate effectively to both technical and non-technical audiences in written and non-written format
• Ability to work with all levels of associates
• Ability to work with minimal supervision in a very dynamic environment
• Ability to learn new systems and tools
• Knowledge of software development life cycle methodologies, processes, and procedures.
• Active listener
• Effective in a variety of formal/informal presentation settings: one-on-one, small, and large groups.
Job Title: Principal Engineer - Data Analytics
Location: Oshkosh, WI/New Hudson, MI
Permanent
Job Description: The Data Science Lead serves at the vital intersection of enterprise business strategy, machine learning innovation, and technical delivery. In this role, you will lead the strategic vision, architectural design, and operational deployment of predictive modeling, advanced analytics, and artificial intelligence solutions across key business units.
Technical Leadership & Solution Architecture
- End-to-End Delivery: Architect, build, and deploy production-grade machine learning models, statistical frameworks, and Generative AI/LLM applications (e.g., RAG architectures, intelligent agents, and fine-tuned models).
- Engineering Standards: Establish best-in-class coding, testing, evaluation, and documentation standards across the team. Conduct regular code and architecture reviews.
- Production MLOps: Partner closely with Data Engineering and DevOps teams to establish robust CI/CD pipelines, automated model monitoring, feature stores, data drift detection, and automated retraining workflows.
- Research & Innovation: Stay abreast of state-of-the-art developments in AI/ML, evaluating emerging methodologies, tools, and frameworks to accelerate organizational capabilities.
Strategy, Road mapping & Stakeholder Management
- Business Translation: Collaborate closely with executive leadership, product managers, operations, and commercial leaders to translate complex business challenges into actionable data science initiatives.
- Strategic Roadmap: Define and prioritize the enterprise AI and analytics roadmap, aligning project timelines, milestones, and deliverables with core business objectives.
- Value Realization & Communication: Articulate complex algorithmic methodologies and quantitative findings in clear, non-technical language to executive stakeholders, driving adoption and demonstrable business impact.
- Governance & Ethical AI: Champion data integrity, security, model explainability, algorithmic fairness, and compliance with organizational policies and international AI safety standards.
People Leadership & Team Development
- Mentorship & Coaching: Lead, coach, and support junior and mid-level data scientists, nurturing individual career trajectories, continuous learning, and technical mastery.
- Agile Project Execution: Foster an agile, delivery-focused sprint cadence, balancing exploratory research with strict milestone deadlines and business commitments.
- Culture & Community: Cultivate an inclusive, collaborative culture that celebrates technical curiosity, innovation, and knowledge-sharing across cross-functional units.
Education & Professional Experience
- Master’s degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative discipline (or equivalent practical experience).
- Minimum of 6+ years of professional experience in data science, predictive modeling, or machine learning engineering.
Technical Expertise
- Expert proficiency in Python (including NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow) and advanced SQL.
- Strong hands-on experience building supervised/unsupervised learning pipelines, deep learning models, natural language processing (NLP), and modern Large Language Model (LLM) architectures.
- Proven track record utilizing enterprise cloud ecosystems (AWS, Azure, or Google Cloud Platform) and distributed compute systems (Databricks, Snowflake, Apache Spark).
- Demonstrated familiarity with model tracking, containerization, and orchestration platforms (e.g., MLflow, Docker, Kubernetes, Kubeflow, Airflow).
- Proficiency in presenting findings via business intelligence and visualization tools (e.g., Power BI, Streamlit).
Soft Skills & Core Competencies
- Strategic Thinking: Ability to see the bigger picture, connecting data capabilities to long-term business strategy.
- Outstanding verbal and written communication skills with the capability to influence senior leadership.
- Pragmatism & Value Orientation: Focus on business utility, avoiding over-engineering in favor of reliable, interpretable, and maintainable solutions.
