Data Scientist I

U.S. Venture, Inc.•Appleton, WI

1d•Onsite

About The Position

As an associate member of our Data Science team, you will contribute to the design, development, and delivery of analytical models that address real business problems. In this role, you will build core modeling skills across prediction, forecasting, and optimization while developing the analytical rigor and engineering discipline needed to deliver production-ready work. A critical focus of this position is learning to operationalize solutions — moving from exploratory analysis to models embedded in live workflows — under the guidance of senior scientists. You will: • Learn and apply core modeling methodologies across supervised, unsupervised, and optimization problems. • Contribute meaningfully to model development cycles and exploratory research, with growing independence over time. • Build engineering discipline by writing clean, testable Python, applying version control, and packaging model services. • Develop communication skills to translate model findings and trade-offs into clear, actionable summaries for stakeholders. This position offers the opportunity to develop as a data scientist within a high-performing intelligence team, building the technical depth and cross-functional judgment needed to grow toward senior-level ownership. This position is located on-site in Appleton, WI.

Requirements

Bachelor’s or Master’s degree in Data Science, Data Analytics, Economics, Statistics, Computer Science or a related field involving problem solving and critical thinking, or equivalent work experience.
0–3 years (or equivalent graduate coursework or project experience) applying data analysis and modeling to structured problems.
Working knowledge of core ML algorithms (regression, classification, tree-based methods); familiarity with cross-validation and model evaluation practices.
Proficiency in Python (pandas, scikit-learn, NumPy) and SQL; familiarity with Git and Docker basics.
Ability to communicate analytical findings and model trade-offs clearly to technical peers and non-technical collaborators.
Strong analytical reasoning and structured problem-solving skills; able to frame ambiguous questions and test hypotheses systematically.

Nice To Haves

Genuine curiosity about business problems; exposure to supply chain, transportation, or energy domains is a plus.

Responsibilities

Applying data science fundamentals: data collection, cleansing, exploratory analysis, and visualization.
Writing Python (pandas, scikit-learn) and SQL to manipulate data and prototype models.
Conducting hypothesis testing and basic statistical validation to support modeling decisions.
Using version control (Git) and foundational software engineering practices (modular code, object-oriented design, Docker basics).
Supervised and unsupervised learning techniques, including classification, regression, and basic clustering.
Time-series and forecasting fundamentals, developed under senior guidance.
Packaging and serving model outputs via Docker and FastAPI endpoints.
Writing model cards and validation reports with clear assumptions, metrics, and evaluation summaries.
Experiment design basics and causal reasoning concepts, developed under mentorship.
Core data engineering concepts: pipeline basics, schema management, and data contract awareness.
Contribute to proof-of-concept spikes and research explorations (e.g., time-series baselines, optimization heuristics, GenAI/RAG experiments).
Participate actively in technical brainstorming sessions
Build small prototypes to test hypotheses and validate modeling approaches before full development.
Support research initiatives in areas such as LLM/RAG evaluation, generative AI applications, and causal ML under senior direction.
Apply and contribute to team standards and reusable components as directed by senior scientists.
Track industry trends and surface relevant ideas with clear evaluation criteria during team discussions.
Champion responsible AI practices in all modeling and experimentation work.
Conduct EDA to ground model design; document data quality issues and surface assumptions before modeling begins.
Contribute to the full model lifecycle — from training and validation through packaging and handoff — with increasing ownership over time.
Understand accuracy, scalability, and time-to-value trade-offs; surface these clearly to senior scientists for prioritization decisions.
Prepare clear model summaries, validation reports, and results presentations for both technical peers and non-technical stakeholders.
Coordinate with platform teams on packaging and deployment requirements for model services.
Actively seek feedback from senior scientists; incorporate code review comments and method guidance to continuously improve modeling quality.
Partner with Data Science Analysts to ensure data correctness and business context before and during modeling.
Build collaborative working relationships across the data science team and with domain SMEs; communicate blockers and progress clearly and proactively.