Dalfen is seeking an Entry Level Applied AI & Data Science Engineer to help build AI-powered tools, analytics solutions, and automation workflows that transform how the firm captures information, analyzes data, and makes decisions. Reporting to the VP, Data Product & Business Solutions, this role will work across Investments, Asset Management, Leasing, Property Management, Accounting, Capital Markets, and Portfolio Management to turn data into practical business applications and AI-enabled workflows.
Position Responsibilities- Build AI-enabled tools, copilots, workflows, and internal applications using Azure OpenAI, Microsoft Copilot Studio, Python, APIs, document AI, retrieval-augmented generation (RAG), vector search, and agent frameworks. Develop solutions such as lease abstraction assistants, due diligence summarization tools, IC memo support, portfolio Q&A assistants, vendor onboarding automation, and internal knowledge systems.
- Apply applied data science techniques to analyze portfolio and operational data, including regression analysis, forecasting, time-series analysis, scenario modeling, sensitivity analysis, and predictive modeling to uncover business insights and risks.
- Develop lightweight business applications including internal web tools, workflow portals, data collection systems, decision support tools, automation workflows, and analytical dashboards through rapid prototyping and iteration.
- Partner directly with business stakeholders to understand workflows, identify pain points, translate needs into technical solutions, gather feedback, and drive adoption of tools into daily operations.
- Support decision-making across investments, asset management, and operations by analyzing performance drivers, market trends, leasing patterns, tenant behavior, and asset-level performance metrics.
- Build and iterate quickly on AI, automation, and analytics solutions with a focus on measurable business impact, adoption, and reduction in manual work.
Skills & Experience- Strong proficiency in Python, SQL, APIs, data analysis, and statistical modeling. Experience with machine learning fundamentals, data visualization, and AI/LLM platforms.
- Familiarity with Azure, OpenAI, Anthropic, Power Platform, Snowflake, Postgres, Git, and modern cloud/data tools.
- Strong understanding of statistical methods including regression analysis, hypothesis testing, forecasting, time-series modeling, scenario analysis, and Monte Carlo simulation.
- Ability to build AI-enabled workflows, automation tools, and lightweight applications using modern development frameworks and APIs.
- Strong communication skills with the ability to translate technical concepts into business language and influence decision-making.
- High ownership mindset with strong curiosity, intellectual humility, and ability to operate in ambiguous environments.
- Comfort working directly with business users and iterating quickly based on feedback.
