Principal Data Scientist

U.S. Venture, Inc.•Appleton, WI

1d•Hybrid

About The Position

As the most senior individual contributor on our Data Science team, you will set the technical direction for how U.S. Venture applies advanced data science, machine learning, and emerging AI capabilities to solve the most complex problems in distribution and supply chain. You will operate as a hands-on technical leader—personally architecting and building the highest-impact models—while shaping the analytical strategy, raising the bar on engineering rigor, and developing the next generation of data scientists. Your deep command of supply chain and distribution strategy, combined with mastery of modern AI techniques and a strongly collaborative approach, will be instrumental in turning data science into a durable competitive advantage for U.S. Venture and its operating companies. This role will ideally be located in Appleton, WI, however, we are open to considering remote/hybrid candidates based on the relevancy of experience. On-site time would be required in Appleton, WI.

Requirements

Bachelor’s or Master’s degree in Industrial Engineering, Industrial Management, Operations Research, Data Analytics, Statistics, Economics, Computer Science, Business Administration, or a related field involving problem solving and critical thinking, or equivalent work experience.
12+ years of relevant experience, including significant hands-on time leading the design, development, and production deployment of advanced statistical, machine learning, and AI models against real distribution, supply chain, or comparably complex operating problems.
Expert ability to develop effective data visualizations that are used by upper management in decision-making situations.
Strong, demonstrable track record of building data science and AI solutions that have delivered material, measurable business outcomes in distribution, supply chain, or comparable operationally complex environments.
Mastery of multiple programming languages, frameworks, and technologies, specifically SQL, Python and/or R, modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and workflow orchestrators (e.g., Airflow, Dagster, or equivalent).
Expert understanding of database concepts, data modeling principles, and modern cloud data platforms (e.g., Azure Data Factory / Synapse / Fabric, GCP BigQuery, Dataflow, and open table formats such as Iceberg, or equivalent).
Strong command of distribution and supply chain strategy and economics, with direct experience applying data science to distribution, transportation, and/or energy operating problems strongly preferred.
Expertise in advanced statistical concepts and modern AI/ML modeling techniques, including deep learning architectures (e.g., transformers, LSTMs, GNNs), reinforcement learning, and applied generative AI / large language model techniques.
Demonstrated ability to mentor and grow data scientists at every level—technical and durable skillsets—and to raise the overall technical bar of a team.
Proven record of creating a collaborative environment that builds a team mentality.
Excellent problem-solving skills and the ability to navigate complex analytical and data-related challenges.
Advanced analytical skills with an emphasis on attention to detail and being able to look at a problem from multiple angles and perspectives.
Strong communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
All applicants must be currently authorized to work in the United States on a full-time basis and must not require U.S. Venture’s sponsorship to continue to work legally in the United States.

Nice To Haves

Deep understanding of the distribution, supply chain, and transportation businesses that U.S. Venture operates in, including the economics, operating constraints, and decision-making contexts that drive value for our internal and external clients.
Data engineering and feature engineering concepts at scale, including pipelines built on modern cloud data platforms (e.g., Azure Data Factory / Synapse / Fabric, GCP BigQuery, Dataflow, and open table formats such as Iceberg).
Optimization model methodologies applied to large-scale distribution networks, inventory positioning, routing, and labor allocation problems.
Forecasting model development, lifecycle management, and continuous improvement across demand, supply, and operational signals.
Designing and deploying models into production with the surrounding MLOps practices—CI/CD, monitoring, drift detection, retraining, and responsible-AI guardrails.
Direct experience applying data science to distribution, transportation, and/or energy operating problems.

Responsibilities

Setting the standard for engineering quality and coding practices used by the Data Science Team, while personally producing production-grade work in the languages used at U.S. Venture (SQL, R, Python) and the surrounding tooling for testing, version control, and deployment.
Setting the technical direction for data science innovation across the enterprise and to be the most senior technical voice in shaping where the team places its bets.
Continuously advancing our modeling techniques through R&D—improving accuracy, runtime performance, scalability, and explainability—and for personally tackling the problems that no one else on the team can.
Defining and shepherding the R&D portfolio for the Data Science Team, sequencing the experiments and proofs that will be executed by Lead and Senior team members and ensuring those experiments translate into production capability.
Personally architecting—and in the highest-stakes cases personally building—the most complex models, simulations, optimizations, and AI-enabled solutions that drive material business decisions.
Maintaining an active external network with peers and researchers at the leading edge of data science and AI—academia, partner labs, vendors, and the broader practitioner community—and will translate that signal into concrete capability for the Data Science Team and U.S. Venture.
Continuously evaluating new platforms, frameworks, and AI capabilities (including foundation models, agentic patterns, and adjacent emerging technologies) and to make the call on what U.S. Venture should adopt, pilot, or pass on.
Personally executing the highest-stakes, most technically demanding projects in the Data Science portfolio—the work that requires the deepest technical judgment and where success or failure has the largest business consequence.
Partnering directly with Data & AI leadership to shape the multi-year analytical strategy, R&D investments, and the integration of AI into the broader Enterprise Platform.
Being the final technical authority on which modeling approach is used for the team’s most significant work, and is accountable for the rigor and defensibility of that choice in front of senior leadership.
Leveraging the full range of statistical, machine learning, and AI techniques to create new analytical products and capabilities for U.S. Venture and its operating companies.
End-to-end forecast modeling which includes Modeling the dataset, Evaluating multiple modeling techniques, Building and orchestrating a pipeline that deploys final model to production.
Building and executing optimization models for the most complex distribution and logistics network problems—multi-echelon inventory, routing, network design, capacity, and labor.
Developing and deploying simulation and digital-twin models that allow internal and external clients to evaluate outcomes under uncertainty and make better strategic and operational decisions.
Communicating outcomes, tradeoffs, and recommendations to senior leadership—including executive, board, and external client audiences—with the credibility to influence material business decisions.
Setting the standard for technical documentation and design review across the Data Science Team, and serving as the final reviewer on the team’s most consequential work.
Partnering closely with Engineering, Architecture, Business Analytics, the business unit operating teams (including U.S. AutoForce, Breakthrough, and the Energy businesses), and external partners—ensuring the Data Science roadmap is tightly coupled to the Enterprise Platform, distribution strategy, and business outcomes across a diverse multi-BU portfolio.
Working with all team members to lead the continuous improvement of the team’s engineering, modeling, and review practices.
Actively mentoring and developing Lead, Senior, and earlier-career data scientists—bringing new concepts, techniques, and methodologies to the team and investing in the long-term growth of the people who will be the next generation of senior practitioners.
Being the team’s primary educator on emerging techniques and AI capabilities—running working sessions, code reviews, design reviews, and worked examples that raise the technical ceiling of the entire group.