Principal Data Scientist

## Principal Data Scientist Apply locations:
WI - Appleton time type:
Full time posted on:
Posted Todayjob requisition :
R7492##
** POSITION SUMMARY
** 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.##
** JOB RESPONSIBILITIES
** Development:
* The expectation is that this individual will join the team as a recognized expert with mastery across the following:  + Understanding of core processes: data collection, cleansing, data models, data modeling and data visualization.  + 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.  

+ 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.  + Advanced statistical and machine learning modeling techniques, including classification, regression, deep learning, reinforcement learning, and modern generative AI / large language model techniques.  

+ Data engineering and feature engineering concepts at scale, including pipelines built on modern cloud data platforms (e.g., Azure Data Factory / Synapse / Fabric, GCP Big Query, 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.

Innovation
* The Data Science Team is one of the teams at the forefront of innovation at U.S. Venture. This individual will be expected to set 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.
* This individual will be accountable for 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.  + They will define and shepherd 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.
* This individual will be expected to push the art of the possible, generate the ideas that define our multi-year analytical roadmap, and pull AI and other emerging technologies into how U.S. Venture solves real distribution and supply chain problems.
* This individual will personally architect—and in the highest-stakes cases personally build—the most complex models, simulations, optimizations, and AI-enabled solutions that drive material business decisions.
* This individual will maintain an active external network with peers and researchers at the leading edge…