Data Scientist II

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 a critical thinker who can work independently, solving ambiguous problems with sound judgment and minimal direction, while remaining highly collaborative.

• Analyze data to support (or disprove) a thesis – dig into data, form hypotheses, and let evidence guide your conclusions.
• Select and implement the right tools for the job – choose appropriate models over transformer if simpler methods suffice.
• Build, train, test, and validate models – from algorithm selection to hyperparameter tuning to rigorous evaluation.
• Engineer models into production – ensure models run reliably in the real world, on real infrastructure, serving real customers.
• Document your work – maintain clear documentation for models, testing protocols, and decision rationale.
• Monitor and improve models in production – watch for model drift and act when retraining or rebuilding is needed.
• Explore agentic and reasoning systems – evaluate semi‑autonomous systems for practical usefulness.
• Perform other duties as assigned or apparent.

• 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/DL models in production.
• Strong scientific foundation in linear algebra, calculus, probability, and statistical inference.
• Understanding of prompt engineering, RAG architectures, fine‑tuning approaches, and embedding models.
• Mastery of supervised/unsupervised learning techniques: regression, classification, clustering, dimensionality reduction, ensemble methods.
• Ability to evaluate LLM outputs critically and design appropriate guard‑rail systems.
• Familiarity with tokenization, context windows, and inference optimization.
• Deep learning expertise with CNNs, RNNs/LSTMs, transformers, and attention mechanisms.
• Experience implementing reinforcement learning algorithms (Q‑learning, policy gradients, actor‑critic, multi‑armed bandits).
• Understanding of reward shaping, exploration vs. exploitation trade‑offs, and temporal difference learning.
• Experience with model testing frameworks, evaluation, validation strategies, and documentation.

• Strong Snowflake/SQL skills and experience with large datasets.
• Proficiency with pandas, Num Py, and data manipulation at scale.
• Data quality assessment, cleaning, and validation.
• Production‑quality Python coding.
• Feature engineering and data preprocessing at scale.
• Model serving patterns: batch inference, real‑time APIs, streaming.
• Deployment and maintenance of production models.
• AWS services:
  Bedrock, Sage Maker, Lambda, S3, EC2, Step Functions, Cloud Watch, EKS.
• Containerization (Docker) and orchestration basics.
• Infrastructure‑as‑code using CDK or Terraform.
• Git, JIRA, Confluence, Slack, and collaboration practices.
• Experience with Jupyter notebooks and Python packages for production.
• PyTorch and/or Tensor Flow, scikit‑learn, XGBoost, Light
  
  GBM, autogluon, Catboost.
• MLflow, Weights & Biases, or similar experiment tracking.

• Autonomous or semi‑autonomous AI systems.
• Agent frameworks (Strands, Agent Core, Lang Chain) and multi‑step reasoning architectures.
• Planning algorithms, state management, decision‑making under uncertainty.
• Image classification, object detection, or segmentation.
• Transfer learning and pretrained vision models.
• Image preprocessing, augmentation, and feature extraction.
• Background in real estate, mortgage, financial services, or logistics.
• Valuation models, risk scoring, pricing algorithms.
• Time‑series forecasting or geospatial analysis.
• CI/CD pipelines for ML workflows.
• Model…