About the role

Role Overview

As a Data Scientist at PLACE, you’ll own data science and machine learning work end-to-end with minimal oversight on a small agile team. The work spans computer vision, valuation modeling, generative AI-powered search, traditional ML, and agentic/reasoning systems, taking models from analysis through production deployment, monitoring, and iteration.

Responsibilities

Analyze data to support or disprove hypotheses using evidence over confirmation bias
Select and implement appropriate modeling approaches, from gradient boosting to transformer-based methods
Build, train, test, and validate models including hyperparameter tuning and rigorous evaluation
Engineer models into production to run reliably on real infrastructure (serving real customers)
Document models, testing protocols, and decision rationale
Monitor models in production and determine when to retrain, rebuild, or rethink due to drift
Explore agentic and reasoning systems, separating useful capabilities from hype

Requirements

Bachelor’s degree or equivalent experience
3+ years relevant experience, including 3–5+ years hands-on AI experience (LLMs and production ML/DL)
Strong foundations in linear algebra, calculus, probability, and statistical inference
Hands-on with LLMs via API/SDK including prompt engineering, RAG architectures, fine-tuning, and embedding models with critical evaluation and guardrails
Experience across supervised/unsupervised learning: regression, classification, clustering, dimensionality reduction, ensembles
Deep learning experience: CNNs, RNNs/LSTMs, transformers, attention mechanisms
Reinforcement learning experience (e.g., Q-learning, policy gradients, actor-critic, multi-armed bandits), including reward shaping and exploration/exploitation
Python for production-quality engineering
Snowflake/SQL and comfort with large datasets
Working knowledge of AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS) plus Docker and infrastructure-as-code
Proven production deployment experience with ongoing model health/iteration

Nice-to-haves

Experience with experiment tracking tools such as MLflow and/or Weights & Biases
Familiarity with ML pipelines, feature engineering, and serving patterns (batch/real-time/streaming)
Git and collaboration practices; familiarity with Jira, Confluence, Slack

About PLACE

PLACE is a hypergrowth startup building a category-defining company at the intersection of real estate, technology, business services, and the consumer. The role sits at the core of scaling the company’s product using data science and AI, including computer vision, valuation modeling, and generative AI search.