Data Science Associate in Long

Cerberus Residential Opportunities Investment Platform Associate

Founded in 1992, Cerberus is a global leader in alternative investing with approximately $70 billion in assets across complementary credit, private equity, and real estate strategies. We invest across the capital structure where our integrated investment platforms and proprietary operating capabilities create an edge to improve performance and drive long-term value. Our tenured teams have experience working collaboratively across asset classes, sectors, and geographies to seek strong risk-adjusted returns for our investors.

For more information about our people and platforms, visit us at

Cerberus established our Residential Opportunities investment platform in 2008 to capitalize on opportunities to acquire distressed residential mortgage-backed securities following the Global Financial Crisis. Over the past decade, our Residential Opportunities platform has continued to build upon its expertise in residential debt and evolved to earn recognition as an innovator and market leader in securitizing performing residential real estate loans and investing in single-family rental real estate (SFRs).

As an Associate at Cerberus Operations and Advisory, you will help the team expand its analytic and operational capabilities across all aspects of Cerberus' Residential Real Estate strategy, from analyzing hard asset and capital markets transactions to supporting operational excellence. The role operates within a startup environment in the strategy with direct interactions to senior leaders and portfolio managers of the fund.

You will use data science and computer science skills to create proof of concepts, stand up infrastructure, and support research projects. You will partner with leaders across portfolio companies to build and execute investment and operational strategies that drive growth. Success in this role requires comfort operating in a fast-moving, high-ambiguity environment where priorities shift and many problems will not have a predefined path to solution.

This is not a traditional data science / software engineering position. While strong technical skills are essential, the primary responsibility is identifying opportunities, validating ideas, and building solutions that create business impact. Successful candidates are scrappy, excited to move fluidly between analytics, software development, experimentation, and business communication, rather than operating within a narrowly defined engineering function.

• Identify and solve high-impact business problems by partnering with senior leadership to translate strategic opportunities, operational challenges, and investment ideas into actionable technology, data, and analytics solutions.
• Own projects end-to-end, from problem definition and solution design to development, deployment, and adoption. Gather requirements, align stakeholders, measure outcomes, and drive initiatives to completion.
• Build and validate new ideas through rapid prototyping and proof-of-concepts. Develop applications, analytical tools, AI-powered solutions, and workflows that test hypotheses, evaluate opportunities, and demonstrate business value before broader deployment.
• Develop software and analytical tools across the full technology stack. Build frontend applications, backend services, data pipelines, APIs, databases, automations, and supporting infrastructure, while balancing speed, maintainability, and scalability.
• Apply AI and automation capabilities to create leverage across the organization. Contribute to agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features. Help build evaluation, observability, and guardrail mechanisms for AI agents, ensuring reliability, safety, and measurable quality as agent behavior translates into product functionality.
• Create leverage through automation, abstraction, and tooling. Identify repetitive or inefficient processes and develop technology solutions that improve productivity, streamline workflows, increase transparency, and reduce operational friction across the organization.
• Design and optimize data infrastructure and data models, including relational databases, data architecture, schema design, query optimization, and systems that support analytical and operational workflows.
• Analyze structured and unstructured data related to residential real estate, property management, and credit underwriting operations to uncover and display insights that inform investment decisions.
• Collaborate across business and technical functions to develop practical solutions, communicate findings clearly, and ensure successful implementation of initiatives.
• Continuously expand expertise in real estate, investing, technology, data science, AI, and emerging tools. Bring intellectual curiosity, a strong learning mindset, and a willingness to explore new approaches that can create business value.

• 3-4 years of work…