Head of Fund Analytics & Automation — Credit Fund

Xsolla

2d • Onsite

About The Position

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 Xsolla transaction data — and that data edge is the foundation of this role.
Strong SQL, Python, and production automation skills are essential, along with real experience in portfolio analytics, market risk, or credit. The ability to ship audit-grade systems and then defend the numbers in front of LPs and borrowers will be key to your success in this role. The role starts as a builder and becomes the owner-operator of the platform, with a growing seat in underwriting decisions and, as the platform and deal volume grow, scope to hire and lead a small team.
If you're passionate about applying AI-governed automation to private credit and love building the systems that let a lean team punch far above its weight, we would love to hear from you!

Requirements

• 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, audit trails, PII handling.
• Finance background: degree in finance or quantitative field plus real experience in portfolio analytics, market risk, or credit — able to hold their own in an underwriting or investor conversation.
• Strong written and spoken English; comfortable presenting to senior external audiences.

Nice To Haves

• Direct exposure to private credit, lending, or fund operations.
• Forecasting and statistical modeling track record (capacity planning, SLA/risk forecasting, or similar).
• Experience with embeddings / semantic search and multi-model AI setups.
• Web development ability (React / TypeScript or similar) for internal tools and dashboards.
• Experience in audited or regulated environments (SOC 2, fund audits, or equivalent).

Responsibilities

• 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 defined schema, low-confidence results routed to human review, and a complete audit trail suitable for LP due diligence.
• Build the ingestion and storage layer for borrower financials and payments telemetry (including Xsolla transaction data) — PostgreSQL or equivalent, ETL pipelines, materialized views, documented data contracts.
• Develop credit scoring and forecasting models in Python, with proper train/test discipline, leakage and PII exclusion, and ongoing monitoring for drift and degradation.
• Maintain evaluation and regression checks so model and automation quality is measured continuously, not assumed.
• Build and operate the investor pipeline: CRM automation, conference and referral pipeline tracking, follow-up orchestration, and data room preparation and upkeep.
• Instrument the pipeline so we always know conversion rates, stage aging, and next actions per LP.
• Ship reporting for both audiences: fund-level metrics for investors, facility-level metrics for borrowers.
• Numbers must reconcile to source systems and be reproducible outside the BI tool — audit-grade, not demo-grade. Tableau / Power BI or a lightweight web dashboard, whichever fits.
• Budget caps and cost monitoring on all AI-assisted automation; regression canaries before changes ship.
• Access control and data protection appropriate for fund data: deny-by-default permissions, audit logs, strict handling of LP identities, borrower financials, and deal terms.
• Documented runbooks and handover-ready documentation as part of "done" — the stack must be operable by someone other than its author.
• Move into an operating role on the underwriting side: portfolio monitoring, covenant and collateral tracking, scenario and stress analysis.
• Extend the platform to other investment types and support the capital formation team with the same pipeline and reporting infrastructure.
• Join investor and borrower meetings, present the dashboards and the numbers behind them, and field diligence questions directly.
• Respond to LP operational due diligence questionnaires on the data and automation stack.

Benefits

• 100% company-paid medical, dental, and vision plans
• Unlimited Flexible Time Off
• Personalized career roadmap
• Professional development through training and educational opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Senior