Your OpportunityThis is YOUR CHANCE to shape the backbone of a company that's scaling rapidly and innovating boldly. As our Data Scientist, you'll own data science and machine learning work end-to-end, with minimal oversight, on a small, agile team where computer vision, valuation modeling, generative AI-powered search, traditional ML, and agentic/reasoning systems are core to the product. You'll partner closely with your Manager, Data Science, and collaborate across a tight-knit team to take models from analysis through production deployment, monitoring, and iteration. If you thrive in complexity, entrepreneurial problem-solving, and believe in scaling through tech and AI, this is your PLACE.

What You're Great AtYou form hypotheses and let evidence guide your conclusions - you value intellectual honesty over confirmation bias, and you can explain uncertainty clearly rather than hiding behind surface-level metrics. You know when to reach for a well-tuned gradient boosting model and when a transformer-based approach is the right call, and you have a strong scientific foundation in linear algebra, calculus, probability, and statistical inference to back that judgment up.

You've worked hands-on with LLMs via API/SDK, and you understand prompt engineering, RAG architectures, fine-tuning, and embedding models well enough to evaluate outputs critically and design real guardrails. You're comfortable across supervised and unsupervised learning - regression, classification, clustering, dimensionality reduction, ensemble methods - and deep learning, including CNNs, RNNs/LSTMs, transformers, and attention mechanisms. You've implemented reinforcement learning approaches (Q-learning, policy gradients, actor-critic, or multi-armed bandits) and understand reward shaping and the exploration/exploitation tradeoff.

You write clean, production-quality Python, you're strong in Snowflake/SQL and comfortable with large datasets, and you know your way around AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, and infrastructure-as-code. You've deployed models to production and kept them healthy over time - not just shipped and walked away.

What You'll Do

• Analyze data to support or disprove a thesis, letting evidence guide conclusions over confirmation bias
• Select and implement the right tools for each problem, from gradient boosting models to transformer-based approaches
• Build, train, test, and validate models - from algorithm selection through hyperparameter tuning and rigorous evaluation
• Engineer models into production so they run reliably on real infrastructure, serving real customers
• Document models, testing protocols, and decision rationale for the team
• Monitor and improve models in production, knowing when to retrain, rebuild, or rethink as data and performance drift
• Explore agentic and reasoning systems, helping the team separate what's genuinely useful from hype in semi-autonomous, planning AI
• Other duties as assigned or apparent

What You'll Need

• Bachelor's degree or equivalent experience
• 3+ years of prior work-related experience, including 3-5+ years of hands-on AI experience (LLMs like GPT, Claude, Qwen, or similar; building and deploying ML/DL models in production)
• Hands-on experience with PyTorch and/or TensorFlow, scikit-learn, XGBoost, LightGBM, AutoGluon, CatBoost, and experiment tracking (MLflow, Weights & Biases)
• Experience with model testing frameworks, evaluation, validation, and documentation
• Familiarity with ML pipelines, feature engineering, and model serving patterns (batch, real-time, streaming)
• Git and collaborative development practices; working familiarity with Jira, Confluence, Slack, and Jupyter

Nice to Haves

• Experience building autonomous or semi-autonomous AI systems; familiarity with agent frameworks (Strands, AgentCore, LangChain) or reasoning architectures (ReAct, chain-of-thought, MCP)
• Understanding of planning algorithms and decision-making under uncertainty
• Experience with image classification, object detection, or segmentation, and transfer learning
• Background in real estate, mortgage, financial services, or logistics (valuation models, risk scoring, pricing algorithms)
• Familiarity with time series forecasting or geospatial analysis
• Experience with CI/CD for ML, model versioning, A/B testing, canary deployments, and drift monitoring

Compensation: $135,000-$170,000, depending on experience