Head of Fund Analytics & Automation — Credit Fund

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 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!

ABOUT US

Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.

For more information, visit xsolla.com.

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 defined schema, low-confidence results routed to human review, and a complete audit trail suitable for LP due diligence.
Scoring and underwriting data layer
- 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.
Fundraising / LP pipeline
- 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.
Investor and borrower dashboards
- 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.
Reliability, cost, and security of the stack
- 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.
Phase 2: Operate (post-close)
- 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.
Client and investor facing
This is not a back-office role. The person will join investor and borrower meetings, present the dashboards and the numbers behind them, and field diligence questions directly. Fluency in finance conversations is as important as engineering. The person will also respond to LP operational due diligence questionnaires on the data and automation stack.

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, 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.

Preferred

- 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).