[Hiring] Data Scientist @PLACE Corporate Careers

Role Description

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.

Qualifications

• 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

Requirements

• Form hypotheses and let evidence guide conclusions
• Value intellectual honesty over confirmation bias
• Explain uncertainty clearly rather than hiding behind surface-level metrics
• Know when to use a well-tuned gradient boosting model vs. a transformer-based approach
• Strong scientific foundation in linear algebra, calculus, probability, and statistical inference
• Comfortable across supervised and unsupervised learning — regression, classification, clustering, dimensionality reduction, ensemble methods
• Deep learning experience, including CNNs, RNNs/LSTMs, transformers, and attention mechanisms
• Implemented reinforcement learning approaches (Q-learning, policy gradients, actor-critic, or multi-armed bandits)
• Understand reward shaping and the exploration/exploitation tradeoff
• Write clean, production-quality Python
• Strong in Snowflake/SQL and comfortable with large datasets
• Familiar with AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, and infrastructure-as-code
• Experience deploying models to production and maintaining them over time

Benefits

• Competitive salary: $135,000–$170,000, depending on experience
• Work from the PLACE you work best — at home, in an office, or on the move
• PTO as needed
• Comprehensive insurance coverage
• 401(k) match
• Stock option grants
• Stock purchase plan
• Every team member is an owner, building the "PLACE" they are proud to call "my company."