AI Data Lead – Alternative Investments

Daedaline•Boston, MA

7d•Hybrid

About The Position

We are building an AI-first investment tool suite designed to transform how alternative asset investors analyze complex opportunities. Our initial focus was on renewable infrastructure, but we are expanding into other high-impact asset classes such as transportation, digital infrastructure, and private credit. This role sits at the intersection of finance, technology, and AI - ideal for professionals who thrive on deep analysis and cross-functional collaboration. As AI Data Lead, you will be responsible for curating and annotating high-quality datasets that feed our AI models, primarily by reviewing and interpreting virtual data room (VDR) materials from infrastructure deals and private credit transactions. You will also craft example investment memos that guide model training and support real-world decision-making. This is not a coding-heavy role - it demands financial, technical, and legal insight more than programming, though a working knowledge of AI/ML concepts is helpful. Your work will directly support investment teams operating in sectors such as solar, battery storage, transportation infrastructure, fiber/digital assets, and bespoke private credit, where project-level diligence is document-heavy and complex.

Requirements

3-5 years of experience in investment analysis, deal execution, or diligence roles, especially in alternative assets - infrastructure, private credit, real assets, or renewables.
Reviewed VDR documents and evaluated technical, legal, and financial aspects of real-world deals.
Bachelor’s degree in Engineering, Finance, Economics, Law, or related fields.
Familiarity with project finance, credit underwriting, cash flow modeling, and asset-specific documentation.
Ability to distill technical and legal materials into clear, executive-ready summaries.
Precision in data labeling and summarization; high stakes for AI model fidelity.
Comfortable working across teams - investment professionals, AI researchers, and product designers.

Nice To Haves

Master’s or CFA/CAIA a plus.
Understanding of supervised learning, annotation pipelines, prompt engineering, or model evaluation.
Competence in financial modeling software (Excel, Python/pandas, or R); exposure to tools like PVsyst, SAM, or credit rating models is a plus.
Insight into renewable energy systems, digital infra, or private debt structures (e.g. asset-backed lending, revolvers, bridge loans).
Familiarity with US infrastructure policy, ESG frameworks, permitting processes, or rating agency criteria.

Responsibilities

Tag and classify investment-critical data from VDRs: documents, tables, charts, and scanned PDFs.
Identify and annotate key deal terms: credit covenants, loan structures, O&M contracts, capital stacks, technical specs, ESG disclosures, regulatory frameworks, etc.
Collaborate with engineers to develop schema for NLP and vision models targeting infrastructure and credit document corpora.
Draft structured, memo-style investment analyses that highlight financial, legal, and operational findings from deals.
Create model reference outputs: short-form investment memos or deal briefs used to train and evaluate AI model performance.
Define annotation protocols and labeling conventions across different asset classes.
Manage external labeling teams or tools: conduct QA checks, clarify ambiguous cases, and iterate on annotation schemas.
Ensure consistency and reproducibility across datasets used to train AI systems.
Work with AI/ML researchers, product teams, and domain experts to ensure that labeled data matches model and user needs.
Translate sector-specific insights into actionable model training inputs.
Contribute to prompt engineering and user experience design for AI-generated investment memos and diligence assistants.