WI · On-site
$120 - $150/hr
... ready AI systems that transform how homes are designed, estimated, and built. What You Will Do ... Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable ...
New
Data Scientist
Madison, WI · On-site
Implement data quality, validation, and AI evaluation frameworks; define reliability metrics, testing protocols, and monitoring controls ensuring outputs are accurate, traceable, and explainable.
Data Scientist
Madison, WI · On-site
Implement data quality, validation, and AI evaluation frameworks; define reliability metrics, testing protocols, and monitoring controls ensuring outputs are accurate, traceable, and explainable.
Data Scientist
Madison, WI · On-site
$90 - $130/hr
... analytics, AI, and machine‑learning solutions that support data‑driven investment ... explainable outputs. * Develop analytics applications and lightweight front‑end interfaces ...
Data Scientist
Madison, WI · On-site
$90 - $130/hr
... analytics, AI, and machine‑learning solutions that support data‑driven investment ... explainable outputs. * Develop analytics applications and lightweight front‑end interfaces ...
Explainable Ai information
What is the difference between Explainable Ai vs Data Scientist?
| Aspect | Explainable Ai | Data Scientist |
|---|---|---|
| Credentials | Typically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are common | Requires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial |
| Work Environment | Works within AI development teams, focusing on model transparency and interpretability | Works across data analysis, model building, and business insights, often in research or corporate settings |
| Industry Usage | Used in AI development, healthcare, finance, and any field requiring transparent AI models | Applied in tech, finance, healthcare, and research for data-driven decision making |
Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.
What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?
Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.
What are the key skills and qualifications needed to thrive as an Explainable AI specialist?
To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.
What is Explainable AI?
Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.
What are popular job titles related to Explainable Ai jobs in Wisconsin? For Explainable Ai jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Wisconsin look for? The top searched job categories for Explainable Ai jobs in Wisconsin are:
What cities in Wisconsin are hiring for Explainable Ai jobs? Cities in Wisconsin with the most Explainable Ai job openings:
$120 - $150/hr
Other
Posted 2 days ago
New
Job description
Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior Applied ML Engineer to be part of revolutionizing these industries.
We are looking for a Senior Applied ML Engineer to design, implement, and scale machine learning systems that power next-generation construction and digital twin solutions. You will apply advanced ML techniques—ranging from computer vision to large language models—to automate critical workflows such as blueprint understanding, 3D model generation, and materials forecasting. This role blends research, engineering, and domain expertise to deliver practical, production-ready AI systems that transform how homes are designed, estimated, and built.
What You Will Do- Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and automated construction workflows.
- Prototype, fine-tune, and assess models for NLP tasks such as classification, entity recognition, and summarization of construction data.
- Build scalable ML pipelines and backend services that integrate into production-grade agents and digital platforms.
- Drive the end-to-end ML lifecycle: from experimentation and training, to deployment, monitoring, and continuous improvement.
- Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable features.
- Collaborate closely with product managers, software engineers, and construction domain experts to solve real-world challenges with measurable business impact.
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
- 5+ years of experience designing and deploying applied ML systems at scale.
- Experience with computer vision (CNNs, object detection, segmentation) and natural language processing (LLMs, embeddings, transformers).
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent).
- Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP).
- Ability to clearly communicate technical concepts to both engineers and non-technical stakeholders.
- Experience applying ML in construction, CAD/BIM, architecture, or digital twin platforms is preferred.
- Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred.