Data Scientist

Your Opportunity At PLACE, we're building a category-defining company at the intersection of real estate, technology, business services, and the consumer. As a profitable, hypergrowth startup, on the path to an IPO, our standards are high, our team is scrappy, and our commitment is to execute the best work of our lives. This 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 At You 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 practic