Head of Fund Analytics & Automation — Credit Fund

2 нед. назад

USAHeadRemote

sqlpythonautomation

Lead and build the fund's data and automation stack for the credit fund, combining engineering and finance expertise.

ABOUT YOU We are looking for a Head of Fund Analytics & Automation who is a hands-on builder, rigorous with data, and fluent in finance conversations to join our Credit Fund team in the office of the Chief Credit Officer. The best candidate will be someone who thrives in a fast-paced, highly collaborative, and exceptionally dynamic setting and is excited to design, build, and operate the fund's entire data and automation stack end to end — and then own it as the fund goes live. The credit fund is being built with a deliberately small team. Instead of hiring several analysts and operations staff, we want one senior technical hire who can do both: engineer the platform and sit across the table from investors and borrowers. Underwriting here is built on real payment telemetry — including

First 6 months — priorities in order - Deal pipeline live: origination intake through credit committee, with the full audit trail. - Scoring data layer: borrower financials and payment telemetry ingested, stored, and under documented data contracts. - LP / fundraising pipeline instrumented, and first investor reporting shipped. Explicitly out of scope: legal documentation, fund administration, and accounting — these sit with external providers. Phase 1: Build (through first close) Deal pipeline management - Design and run the full deal workflow: origination intake, screening, scoring, credit committee, closing. - Implement it as governed automation (workflow orchestration such as n8n or similar, with LLM-assisted steps where they add value): every automated output validated against a defin

Required - 7+ years across data analytics / data engineering, including recent hands-on experience building and running production automation (not prototypes or notebooks). - Proven production experience with LLM-based automation: schema-validated outputs, human review gates, evaluation and regression testing, cost-tiered model routing. - Strong SQL and Python; ownership of a PostgreSQL (or similar) data platform end to end — ETL, materialized views, performance tuning, data contracts. - Workflow orchestration experience (n8n, Airflow, or comparable). - BI and dashboarding: Tableau, Power BI, Qlik, or equivalent web dashboards; a track record of reporting that executives actually used for decisions. - Security discipline for sensitive data: role-based access, deny-by-default policies, audi