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Data Science 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 ...

Amcor

R&D Senior Manager - Data Science

Neenah, WI · On-site

As a key enabler of digital transformation, this leader oversees a multidisciplinary team spanning data science, business intelligence, and systems management-translating complex materials and R&D ...

Amcor plc

R&D Senior Manager - Data Science

Neenah, WI · On-site

As a key enabler of digital transformation, this leader oversees a multidisciplinary team spanning data science, business intelligence, and systems management-translating complex materials and R&D ...

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$36.6K

$119.8K

$191.7K

How much do data science jobs pay per year?

As of Jul 9, 2026, the average yearly pay for data science 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.

Is data science a good career?

Data science is a growing field with high demand for professionals skilled in statistics, programming, and data analysis tools like Python and R. It offers competitive salaries, diverse industry applications, and opportunities for advancement, making it a strong career choice for those with relevant skills and education.

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, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What jobs can a Data Scientist do?

A Data Scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What work do you do as a Data Scientist?

A Data Scientist analyzes large datasets to extract insights, build predictive models, and inform business decisions. They use programming languages like Python or R, and tools such as SQL and machine learning frameworks, often working in collaborative environments with data engineers and analysts.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Appleton, WI? The most popular types of Data Science jobs in Appleton, WI are:
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Data Science jobs near you
Principal Data Scientist

Principal Data Scientist

U.S. Venture, Inc.

Appleton, WI • On-site

Full-time

Re-posted 7 days ago

U.S. Venture rating

7.3

Company rating: 7.3 out of 10

Based on 16 frontline employees who took The Breakroom Quiz

172nd of 358 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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