Data Scientist II

RealPage, Inc.•Richardson, TX

2d•Remote

About The Position

We are looking for a Data Scientist II to help design, build, and deploy machine learning and generative AI solutions that power real world products and decisions. In this role, you’ll work on production AI systems, partner closely with engineering and product teams, and take ownership of meaningful data science initiatives from idea to deployment. This is a hands on, individual contributor role for someone who enjoys shipping models, working with modern AI tooling, and solving real business problems with data.

Requirements

3–6 years of experience in data science, machine learning, or applied AI
A degree (Master's or better preferred) in Computer Science, Data Science or related fields
Strong Python and SQL skills
Handson experience deploying models into production
Familiarity with cloud platforms (AWS, Azure, or GCP)
Experience with modern ML frameworks (PyTorch, TensorFlow, scikitlearn)
Exposure to LLMs, embeddings, and GenAI workflows
Ability to communicate clearly with engineers, product managers, and nontechnical partners

Nice To Haves

Experience with streaming data systems
Experience operating models at scale
Knowledge of MLOps tools and observability practices
Experience building AI solutions that support customer facing products

Responsibilities

Develop, evaluate, and deploy predictive and generative models for real production use cases
Perform feature engineering and data preparation for modeling workflows
Translate business and product questions into analytical solutions
Build and maintain LLM powered features and services
Develop retrieval augmented generation (RAG) pipelines using embeddings and vector databases
Integrate LLMs with APIs and internal tools using structured function calling
Finetune foundation models with parameter efficient approaches (e.g., LoRA)
Evaluate model quality, detect hallucinations, and implement safety guardrails
Use synthetic data to improve model performance, testing, and fairness
Optimize inference performance and cost across different model providers
Deploy and operate machine learning and GenAI models in production
Build CI/CD pipelines for models and data workflows
Monitor performance, data quality, and model drift
Design versioning, rollback, and retraining strategies
Partner with platform and infrastructure teams to ensure reliability and scalability
Build low latency data pipelines and real time decisioning systems
Work with streaming data and event driven architectures
Support systems with strict uptime and response time requirements
Contribute to feature stores used for both real time and batch inference