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Algorithm Scientist Jobs in Wisconsin (NOW HIRING)

Data Scientist Intern Location : De Pere, WI Duration : 6-12 months Position Overview The Data ... Machine Learning Basic understanding of machine learning concepts, algorithms, and methodologies.

Data Scientist Intern Location: De Pere, WI Duration: 6-12 months Position Overview The Data ... Basic understanding of machine learning concepts, algorithms, and methodologies. * Analytical ...

Develop custom data models and algorithms to apply to data sets. * Use predictive modeling to ... At least 6+ years of data science/engineering experience * Strong problem-solving skills with an ...

Provide technical guidance to data scientists and analysts across the organization. * Mentor junior ... Deep expertise in machine learning algorithms, statistical modeling techniques, and predictive ...

Develop custom data models and algorithms to apply to data sets. * Use predictive modeling to ... At least 6+ years of data science/engineering experience * Strong problem-solving skills with an ...

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Algorithm Scientist information

See Wisconsin salary details

$52K

$115K

$141.7K

How much do algorithm scientist jobs pay per year?

As of Jun 3, 2026, the average yearly pay for algorithm scientist in Wisconsin is $115,021.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $141,100.00 per year, depending on experience, location, and employer.

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

To thrive as an Algorithm Scientist, you need a strong background in mathematics, statistics, and computer science, often supported by an advanced degree such as a Master's or Ph.D. in a related field. Proficiency with programming languages like Python or C++, machine learning libraries (e.g., TensorFlow, PyTorch), and experience with data analysis tools are typically required. Strong problem-solving abilities, analytical thinking, and effective communication skills help distinguish top performers in this role. These skills are vital for developing innovative algorithms that solve complex problems, ensuring practical, scalable solutions in technological environments.

How does an Algorithm Scientist typically collaborate with cross-functional teams during the development process?

Algorithm Scientists frequently work alongside data engineers, software developers, and product managers to design and implement solutions. They are often responsible for communicating complex mathematical concepts in accessible terms to ensure alignment across the team. Regular meetings and code reviews are common, allowing for feedback and rapid iteration. This collaborative environment helps ensure that the algorithms developed are both technically sound and practically viable for real-world applications.

What are Algorithm Scientists?

Algorithm Scientists are professionals who design, analyze, and optimize algorithms to solve complex computational problems. They often work in fields like data science, artificial intelligence, finance, and engineering, developing new methods or improving existing ones for processing data efficiently. Their work involves rigorous mathematics, computer science, and research to ensure algorithms are accurate, scalable, and effective for specific applications. Algorithm Scientists may also collaborate with software engineers to implement their solutions in real-world systems.
Senior Data Scientist

Full-time

Posted 3 days ago


Job description

Company Description

The Weather Company provides the best weather insight in the world, and is leading the charge in the growing area of weather decision support for business. We are offering you a unique opportunity to apply and/or develop your mathematical modeling skills on our unique set of weather data. Working with weather data is really unique and amazing; weather is in perpetual evolution, generates petabytes of new data every month, and deeply impacts people and businesses on various timescales. We serve a wide variety of businesses including renewable energies, energy traders, utility companies, insurance, retailers, and consumer product groups. As a consequence you will apply and/or learn a wide variety of statistical techniques including time series analysis, high dimensional clustering, machine learning, data mining and Bayesian modeling.

Job Description

Are you interested in applying machine learning or data mining on problems that truly improve people's life? We're looking for a mathematician/data scientist eager to tackle unique challenges in the realm of predicting weather's impact on business. You will work on a skilled team of passionate data scientists and meteorologists. Examples of projects you may encounter would be anything from predicting the electricity output of a solar park in Arizona, to predicting how much ice cream is going to be sold next week in Chicago.

  • Partner collaboratively with the business and project teams to accomplish tasks/milestones/goals.

  • Research, recommend, and implement statistical post process correction techniques using proprietary forecasts.

  • Demonstrate solutions by developing documentation, flowcharts, layouts, diagrams, charts, etc.

  • Improve operations by conducting systems analysis; recommending changes in policy and procedures.

  • Provide estimates of work effort and impact of projects and tasks, and provide team leadership, as required.

  • Continuously build your knowledge by studying new scientific methodologies and techniques.

  • Play an active role in the product requirements process, giving feedback to product management when challenges arise.

Qualifications
  • MS in Applied Statistics, Mathematics, Econometrics, or other discipline related to Time-Series Analysis, Machine learning and Forecasting, or other related discipline.

  • 3-5 years of relevant professional experience, with demonstrated achievements.

  • Can demonstrate mastery of general scientific computing softwares such as R, MATLAB, Octave, etc.

  • Experience using/implementing non-parametric regression such as  Neural Net, SVM, Random Forest, Projection Pursuit, MARS, Radial Basis Functions, AdaBoost, GLM

  • Experience in Predictive Modeling including Non-Parametric Regression, Bayesian Inference, Hidden Markov Models, Generalized ARMA, or Kalman Filtering is a plus.

  • Experience in non-linear optimisation including Simulated Annealing, Genetic Algorithm, Agent Based Modeling, Particle Swarm, Bee Colony is a plus but not necessary.

  • Knowledge of ensemble learning techniques and probabilistic forecasts is a plus.

  • Programming capabilities including C++, Java, Python is a plus but not necessary.

Additional Information