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About This Role
POSITION SUMMARY
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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
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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 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.
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 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.
+ They are expected to continuously evaluate 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.
Execution
- This individual will personally execute 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.
- They will partner 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.
- This individual is 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.
- The responsibilities this individual also includes:
+ 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.
Collaboration:
- This individual must have outstanding interpersonal and influencing skills, with the ability to build rapport and earn credibility at every level—from engineers and analysts up through the CIO, executive leadership team, and business unit presidents.
- This person will partner 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 mentor and develop 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.
+ Be 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.
QUALIFICATIONS
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Required:
- 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.
DIVISION:
Corporate
*U.S. Venture will not offer sponsorship for employment status (including, but not limited to, H\-1B, TN, E\-3, F1, CPT, OPT, STEM OPT, visa status and other employment‑based nonimmigrant visas) for this position. Accordingly, 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. In general, U.S. Venture does not sponsor candidates for nonimmigrant visas or permanent residency except when there is a specific business need.*
U.S. Venture will not accept unsolicited resumes from recruiters or employment agencies. In the absence of an executed recruitment Master Service Agreement, there will be no obligation to any referral compensation or recruiter fee. In the event a recruiter or agency submits a resume or candidate without an agreement, U.S. Venture shall reserve the right to pursue and hire those candidate(s) without any financial obligation to the recruiter or agency. Any unsolicited resumes, including those submitted to hiring managers, shall be deemed the property of U.S. Venture.
U.S. Venture, Inc. is an equal opportunity employer that is committed to inclusion and diversity. We ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender, gender identity or expression, marital status, age, national origin, disability, veteran status, genetic information, or other protected characteristic. If you need assistance or an accommodation due to a disability, you may call Human Resources at (920\) 739\-6101.
99\-00\-821\-0000
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At U.S. Venture, Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
U.S. Venture, Inc. AI Hiring
U.S. Venture, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Appleton, WI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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