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Data Scientist Jobs in Appleton, WI (NOW HIRING)

U.S. Venture

Principal Data Scientist

Appleton, WI · On-site

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 ...

Newance

Senior Data Scientist

Green Bay, WI · On-site

As a member of our Data Science team, you will play a crucial role in leveraging, building and developing analytical tools to solve complex business problems. You will also be expected to act as ...

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Data Scientist information

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$36.6K

$119.8K

$191.7K

How much do data scientist jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data scientist in Appleton, WI is $119,759.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,100.00 and $132,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks, data visualization tools, and big data platforms like TensorFlow, Tableau, and Hadoop, as well as certifications in data science, are highly valued. Excellent problem-solving skills, curiosity, and the ability to communicate complex findings clearly set outstanding data scientists apart. These skills and qualities are crucial for extracting actionable insights from data, driving business decisions, and collaborating effectively with stakeholders.

What Do Data Scientists Do?

Data scientists collect, confirm, and interpret data to determine useful information for their employer. They help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. The information data scientists get from the records they gather helps businesses make major decisions in critical areas, such as product development, sales and marketing techniques, and client retention. Data scientists are highly educated; the majority of them have at least a master's degrees, and many have doctorates. Data scientists are valuable members of organizations in many different industries, including pharmaceuticals, manufacturing, and banking.

What careers can I do with data science?

Data scientists can pursue careers in fields such as machine learning engineering, data analysis, business intelligence, data engineering, and research roles. These positions often require skills in programming, statistical analysis, and tools like Python, R, or SQL, and may involve working in industries like finance, healthcare, technology, or marketing.

Is a data scientist job still in-demand?

Yes, data scientist roles remain in high demand across various industries due to the increasing reliance on data-driven decision making. Skills in machine learning, statistical analysis, and programming languages like Python or R are highly valued, and the field continues to grow as organizations seek to leverage big data for competitive advantage.

What are Data Scientists?

Data Scientists are professionals who use statistical, analytical, and programming skills to collect, analyze, and interpret large volumes of data. They extract insights and trends from complex data sets to help organizations make data-driven decisions. Data Scientists often work with machine learning, data mining, and big data technologies to build predictive models and solve business problems. Their work bridges the gap between technical data analysis and actionable business strategy.

What does a data scientist do exactly?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use statistical techniques, programming languages like Python or R, and tools such as SQL and machine learning algorithms to interpret data and solve complex problems.

Is 30 too late for data science?

Data scientists can enter the field at any age, including 30 or older, as success depends on skills, experience, and continuous learning. Many professionals transition into data science from different backgrounds by acquiring relevant skills such as programming, statistics, and machine learning through courses or certifications. Age is not a barrier if you develop a strong portfolio and stay current with industry tools and techniques.

What is the difference between Data Scientist vs Data Analyst?

AspectData Scientist
Required CredentialsDegree in Computer Science, Statistics, or related field; often requires advanced degrees
Work EnvironmentResearch and development, predictive modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcare, consulting firms
Common Search & ComparisonOften compared due to overlapping skills in data analysis and modeling

Data Scientists focus on building predictive models, advanced analytics, and machine learning, often requiring higher-level technical skills and education. Data Analysts primarily interpret existing data, generate reports, and support decision-making with descriptive analytics. While both roles analyze data, Data Scientists handle complex modeling and predictive tasks, whereas Data Analysts focus on data interpretation and reporting.

What are some typical projects Data Scientists work on, and how do they collaborate with other teams?

Data Scientists often work on projects such as building predictive models, analyzing large datasets to uncover trends, and developing data-driven solutions to business problems. They regularly collaborate with cross-functional teams, including software engineers, data engineers, and business analysts, to ensure that their insights are actionable and aligned with business goals. Effective communication and teamwork are essential, as Data Scientists frequently need to present complex findings to non-technical stakeholders and incorporate feedback from various departments.
More about Data Scientist jobs
What are the most commonly searched types of Data Scientist jobs in Appleton, WI? The most popular types of Data Scientist jobs in Appleton, WI are:
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Data Scientist jobs near you
Principal Data Scientist

Principal Data Scientist

U.S. Venture, Inc.

Appleton, WI • On-site

Full-time

Re-posted 12 days ago

U.S. Venture rating

7.9

Company rating: 7.9 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

107th of 367 rated retail wholesalers

Job description

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 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
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

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