Data Scientist II

Radian Group Inc. • Bethesda, MD

1d • $98,000 - $148,000

About The Position

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.

Requirements

• Bachelor's Degree or equivalent experience
• 2 or more years of prior work-related experience
• 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.
• Ability to evaluate model performance beyond surface metrics and explain uncertainty clearly.
• 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
• 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

Nice To Haves

• 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

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.

Benefits

• Competitive Compensation: anticipated base salary from $98,000 to $148,000 based on skills and experience. This position is eligible to participate in an annual incentive program.
• 25 days of paid time off annually, which is prorated in the year of hire based on hire date.
• 9 paid holidays + 2 floating holidays.
• Parental leave
• Multiple medical plan choices, including HSA and FSA options, dental, vision, and basic life insurance.
• 401(k) with a top of market company match (did we mention the company match is immediately vested?!)
• Opportunity to participate in Radian’s Employee Stock Purchase Plan (ESPP).
• Homebuyer Perks program helps employees navigate the home searching, buying, selling, and refinancing processes and provides valuable financial benefits to encourage, enable, and support home ownership.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree