Data Scientist II

See yourself at Radian? We see you here too.

At Radian, we see you. For the person you are and the potential you hold. That’s why we’ve embraced a new way of working that lets our people across the country be themselves, be their best and be their boldest. Because when each of us is truly seen, each of us gives our best – and at Radian, we’ll give you our best right back.

See Yourself as a Data Scientist II

The Data Scientist II role sits on a team where data science products are core to what we build. We develop computer vision systems that analyze real estate properties, valuation models that price homes, generative AI that powers smarter property search experiences, and traditional machine learning that drives business decisions. We are also investing in systems that can reason, plan, and operate with increasing autonomy.

This is a mid-level, hands-on individual contributor role for someone who wants end-to-end ownership. We’re looking for someone who is a critical thinker and can work independently solving ambiguous problems with sound judgment and minimal direction, while remaining highly collaborative with teammates. You take full ownership of your work, demonstrating accountability from start to finish, and are self-motivated in driving projects forward without constant oversight. You communicate clearly, listen actively, and remain open to feedback and continuous growth.

As part of a small, agile team, you will contribute end-to-end, rolling up your sleeves to execute rather than operate at a purely strategic level. You are action-oriented, enjoy solving problems, and bring a positive, engaging presence to the workplace—valuing both strong relationships and a sense of fun at work.

Candidates should be prepared to share examples of production-grade models or systems they have owned end-to-end, including what they learned from deployment, monitoring, and iteration. Real estate or mortgage experience is not required; curiosity about how people search for, buy, finance, and value homes is helpful.

See the Primary Duties and Responsibilities

• Analyze data to support (or disprove) a thesis – You'll dig into data, form hypotheses, and let evidence guide your conclusions. We value intellectual honesty over confirmation bias.

• Select and implement the right tools for the job – Not every problem needs a transformer. Some problems just need a well-tuned gradient boosting model. You'll know the difference.

• Build, train, test, and validate models – From algorithm selection to hyperparameter tuning to rigorous evaluation. You'll need solid grounding in math and statistics to evaluate model performance and defend your choices.

• Engineer models into production – This isn't research for research's sake. Your models need to run reliably in the real world, on real infrastructure, serving real customers.

• Document your work – Future you (and your teammates) will thank you. We maintain clear documentation for models, testing protocols, and decision rationale.

• Monitor and improve models in production – Models drift. Data changes. You'll keep watch and know when it's time to retrain, rebuild, or rethink.

• Explore agentic and reasoning systems – We're investing in semi-autonomous systems that can plan and act. You'll help us figure out what's hype and what's actually useful.

• Perform other duties as assigned or apparent.

See the Job Specifications

Basic Education and Prior Work-Related Experience:

Degree Requirement: Bachelor's Degree or equivalent experience

Work Experience: 2 or more years of prior work-related experience

Primary Required Qualifications

• 2-5+ years of hands-on AI experience including working with LLMs (GPT, Claude, Qwen, or similar) via API/SDK and building and deploying ML or DL models in production environments.

• Core to success in this role is the ability to evaluate model performance beyond surface metrics and explain uncertainty clearly. This requires a strong scientific foundation in linear algebra, calculus, probability, and statistical inference.

• Understanding of prompt engineering, RAG architectures, fine-tuning approaches, and embedding models

• Strong command of supervised and unsupervised learning techniques: regression, classification, clustering, dimensionality reduction, ensemble methods

• Ability to evaluate LLM outputs critically and design appropriate guardrail systems.

• Familiarity with tokenization, context windows, and inference optimization

• Deep learning expertise including CNNs, RNNs/LSTMs, transformers, and attention mechanisms

• Practical experience implementing Reinforcement Learning algorithms: Q-learning, policy gradients, actor-critic methods, or multi-armed bandits.

• Understanding of reward shaping, exploration vs. exploitation tradeoffs, and temporal difference learning

• Ability to evaluate and define the appropriate model for each problem based on business requirements. 

• Experience with model testing frameworks, model evaluation, validation strategies, and model documentation

Other Required Qualifications

• Strong Snowflake/SQL skills and experience working with large datasets.

• Proficiency with pandas, NumPy, and data manipulation at scale

• Experience with data quality assessment, cleaning, and validation

• Proficiency writing clean, maintainable, production-quality Python code.

• Familiarity with ML pipelines, feature engineering, and data preprocessing at scale

• Understanding of model serving patterns: batch inference, real-time APIs, streaming

• Experience deploying to production and maintaining models over time.

• Working experience with AWS services: Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS

• Familiarity with containerization (Docker) and orchestration basics

• Experience with infrastructure-as-code using CDK or terraform.

• Git version control and collaborative development practices

• Altassian suite of JIRA and Confluence, Slack for communications

• Jupyter notebooks for exploration, Python packages for production

• PyTorch and/or TensorFlow

• scikit-learn, XGBoost, LightGBM, autogluon, Catboost.

• MLflow, Weights & Biases, or similar experiment tracking

Additional Preferred Experiences

• Experience building autonomous or semi-autonomous AI systems.

• Familiarity with agent frameworks (Strands, AgentCore, LangChain), platforms, tool use patterns, or multi-step reasoning architectures (ReAct, chain-of-thought, MCP)

• Understanding of planning algorithms, state management, and decision-making under uncertainty

• Experience with image classification, object detection, or segmentation

• Familiarity with transfer learning and pretrained vision models.

• Understanding of image preprocessing, augmentation, and feature extraction

• Background in real estate, mortgage, financial services, or logistics

• Experience with valuation models, risk scoring, or pricing algorithms

• Familiarity with time series forecasting or geospatial analysis.

• CI/CD pipelines for ML workflows

• Model versioning, A/B testing frameworks, and canary deployments.

• Monitoring, alerting, and drift detection in production

• Experience with model documentation and governance requirements

See Why You Should Work With Us

The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.

Radian will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance law.

See More About Radian

Radian Group Inc. (NYSE: RDN) is a trusted, global multi-line specialty insurer that helps businesses navigate risk with confidence. Built on financial strength and disciplined risk management, Radian brings clarity to complex risk decisions through its proprietary view of risk and a global perspective.

Visit radian.com to learn how our collaborative and customer-centric culture transforms risk into a world of opportunity. 

Defining Roles for Radian's Future

Understanding the qualities and characteristics that define a Leader and an Employee is important to ​building our future-fit workforce. Radian's future is only as bright as its people. For that reason, our People Plan includes profiles to support the qualities and characteristics that each Leader as well as each Employee should embody upon hire or via development.

EEO Statement

Radian complies with all applicable federal, state, and local laws prohibiting discrimination in employment.  All qualified applicants will receive consideration for employment without regard to gender, age, race, color, religious creed, marital status, gender identity, sexual orientation, national origin, ethnicity, ancestry, citizenship, genetic information, disability, protected veteran status or any other characteristic protected by applicable federal, state, or local law.

An applicant’s criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position.  The material duties include those listed in the “Primary Duties and Responsibilities” section above, as well as the ability to adhere to Company policies, exercise sound judgment, effectively manage stressful situations, work safely and respectfully with others, exhibit trustworthiness, and safeguard confidential information belonging to the Company and its customers.  Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Equal Opportunity Employer Details

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Accommodation

Whether you require an accommodation for the job application or interview process, Radian is dedicated to a barrier-free employment process and encourages a diverse workforce. If you have questions about the accommodation process, please e-mail careers@radian.com.

Please note that you may redact or remove age-related information that identifies your age, date of birth, or dates of attendance at or graduation from an educational institution on any additional application materials you submit as part of the application. Additional application materials include but are not limited to, resumes, CVs, transcripts, or certifications.