Digital Technology Data Scientist Lead

Digital Technology Data Scientist Lead
Oshkosh, WI or New Hudson, Michigan

The roles are described as hybrid: 3 days onsite and 2 remote, based in Oshkosh, Wisconsin, or New Hudson, Michigan. Relocation is acceptable if the candidate is willing to relocate; the manager said these are not commuting positions.

Core requirements

• 5+ years of experience; 6 years may be appropriate, according to the manager.
• The same core profile as R50808: AI-savvy and business-savvy, with strong business relationships and experience working directly with business stakeholders not solely back-end development.
• Ability to manage work end to end and personally deploy and deliver the solution.

Shared considerations

• Both roles are individual-contributor positions, not manager roles.
• The manager is open to candidates who began in a business function or as a business analyst and later moved into IT, analytics, or data science, as well as candidates from consulting.
• Mentoring and executive-level communication are more central to the Principal role than to the Lead role

" The Principal Data Scientist: This is our technical authority and strategic lead. Their focus is defining the technical roadmap and AI/ML architecture across our business segments (like Supply Chain and Manufacturing). They need strong business acumen to link algorithmic design to industrial impact, though they must still be ready to take a "roll up your sleeves" approach to the work.

" The Data Scientist Lead: This is our hands-on delivery and SME leader. They focus on the end-to-end building and deploying of production-grade models (including GenAI/LLMs) and establishing MLOps/engineering standards. They are also responsible for directly coaching and mentoring our junior-to-mid-level data scientists.

We want to steer clear of pure researchers who just want to do theoretical work. We need people who love building real, usable solutions!

Our Principal needs to be comfortable presenting to leadership, and our Lead needs to be great at partnering with application teams to get models deployed.

SUMMARY
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 GCP) 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.