Data Scientist

ABOUT CIM GROUP: CIM is a community-focused real estate and infrastructure owner, operator, lender, and developer. Our team of experts works together to identify and create value in real assets, benefiting the communities in which we invest. Back in 1994, our three founders focused on projects in Southern California neighborhoods. Today, we are a diverse team of 900+ employees with projects across the Americas. Our projects have delivered jobs; created comfortable places to live, work, and relax; and provided necessary and sustainable infrastructure. Our focus on enhancing communities is unwavering, and we strive to make an even greater impact in the years to come. Join us and make an impact today!
POSITION PURPOSE:

The Senior Data Scientist will be a pivotal member of our Enterprise Data Management team, playing a critical role in transforming raw, complex data into actionable intelligence that drives superior investment outcomes and operational efficiencies across the firm and its portfolio companies. The role is strategically positioned at the intersection of deep financial domain expertise and cutting-edge data science, requiring a proactive approach to problem-solving and the ability to translate complex technical findings into clear, strategic recommendations for investment teams and senior leadership. In this role, you will bridge the gap between advanced predictive modeling and production engineering, owning the full model lifecycle from feature engineering and offline experimentation to containerized deployment, CI/CD automation, and post-deployment monitoring. The work will directly contribute to, and be enabled by, the firm’s foundational data assets and policies.

RESPONSIBILITIES:

• Conduct in-depth analysis of large datasets to identify trends, patterns, and insights that can inform business strategy.
• Translate complex findings into clear, actionable recommendations.
• End-to-end machine learning delivery, including architecting, training, evaluating, and deploying production-grade predictive models and statistical algorithms across vast, disparate datasets.
• Design and implement sophisticated AI-powered algorithms and predictive models to continuously monitor, analyze, and optimize company performance across all three investment platforms.
• Model governance & monitoring; implement automated monitoring for production models to detect data drift, concept drift, feature skew, and latency degradation. Establish retraining triggers and model registry governance.
• Own the deployment and operationalization of ML models using MLflow, containerization (Docker), and automated CI/CD pipelines. Transition models from notebook prototypes into scalable batch jobs or low-latency REST APIs.