基金分析与自动化主管 — 信用基金
Head of Fund Analytics & Automation — Credit Fund
你的情况
我们正在寻找一位基金分析与自动化负责人,他/她应是亲力亲为的建设者,对数据严谨,并能流畅地进行财务对话,加入首席信用官办公室的信用基金团队。最佳候选人应能在快节奏、高度协作且极具动态的环境中茁壮成长,并热衷于从头设计、构建和运营基金的整个数据和自动化系统——并在基金上线后负责运营。
该信用基金由一支刻意精简的团队打造。我们不招聘多名分析师和运营人员,而是希望有一位高级技术人才,既能构建平台,又能与投资者和借款人面对面交流。这里的承销基于真实的支付遥测数据——包括Xsolla交易数据——而这一数据优势是该职位的基础。
强大的SQL、Python和生产自动化技能是必不可少的,同时需要在投资组合分析、市场风险或信用领域有实际经验。能够交付审计级系统,并在有限合伙人和借款人面前捍卫数据,将是该职位成功的关键。该职位最初以建设者身份开始,随后将成为平台的所有者兼运营商,并随着平台和交易量的增长,逐步参与承销决策并有机会招聘和带领一个小团队。
如果你热衷于将AI治理的自动化应用于私募信贷,并热爱构建让精简团队发挥超常实力的系统,我们期待你的加入!
关于我们
Xsolla是一家全球性的商业公司,拥有强大的工具和服务,帮助开发者解决视频游戏行业的固有挑战。从独立开发到AAA级别,公司与Xsolla合作,帮助他们资助、分发、推广和变现游戏。基于对游戏未来发展的信念,Xsolla致力于汇聚机会,并持续为创作者提供新的资源。总部和注册地均位于美国加利福尼亚州洛杉矶,Xsolla作为交易商代表运营,并已帮助超过1500名游戏开发者在全球范围内触达更多玩家并增长业务。随着更多盈利路径和获胜方式的出现,开发者拥有了享受游戏所需的一切。
更多信息,请访问 xsolla.com。
福利
我们热衷于为团队营造一个支持性的环境,因此我们优先关注员工的身心健康和情感福祉。
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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.
Benefits
We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.
Equal Employment Opportunity Statement
Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.
Criminal History Consideration
For the Head of Fund Analytics & Automation — Credit Fund position, we will conduct a background check that may include the following:
- Criminal history check
- Employment verification
- Education verification
- Credit history check
Relevance to Job Responsibilities
The background check is relevant to this position because of the following role responsibilities:
- Handling sensitive financial information and supporting fund analytics, financial models, and deal data
- Accessing confidential company data
- Ensuring compliance with regulatory requirements
Rights Under the Fair Chance Act
Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.
By submitting the following job application form, you consent to Xsolla processing your data for career-related inquiries and potential employment opportunities. We process your data in accordance with this Xsolla Privacy Notice for Job Applicants. Please direct any inquiries regarding your data privacy to careers@xsolla.com.
Responsibilities
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.
Qualifications and Skills
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).