🕒 7 days ago
Research, implement, and validate machine learning enhancements to unsecured underwriting models, improving predictive performance and credit decisioning while ensuring reliable, rigorous production deployment.
Principal / Staff Data Scientist
📅 Jul 29
Machine LearningStatisticsSupervised LearningTransfer Learning
📜 Description
- Architect advanced machine learning solutions and guide technical direction across the ML ecosystem.
- Lead complex ML initiatives and build scalable, production-ready systems.
- Create, train, and fine-tune models, evaluating performance and investigating failures.
- Own production models, including deployment, monitoring, drift detection, and retraining.
- Mentor ML engineers and elevate engineering standards.
🛠️ Requirements
- Advanced degree in statistics, machine learning, or a related field; experience leading high-impact data science initiatives at billions-of-transactions scale.
- Hands-on experience creating, training, and fine-tuning models, with the ability to explain data, objectives, evaluation results, and model failures.
- Production model ownership, including deployment, monitoring, drift detection, retraining, and latency budgets.
- Production experience with classical ML for fraud or anomaly detection, recommendations, or churn/LTV.
- Knowledge of supervised and transfer learning, neural networks, and basic LLM use and limitations.
- Familiarity with MLOps foundations, including feature stores, experiment tracking, model registries, and continuous training pipelines.
- Experience with model serving and inference optimization, including vLLM-class serving or quantization.
✨ Benefits
- Company-paid medical plan
- Company-paid dental plan
- Company-paid vision plan
- Unlimited Flexible Time Off
- Personalized career roadmap for US employees
- Professional development through training and educational opportunities
Full job description
ABOUT YOU
We are looking for an accomplished Principal Machine Learning Engineer to join our global ML organization. In this role, you will drive innovation across our machine learning ecosystem, architect advanced ML solutions, and mentor junior ML engineers around the world. You will play a key part in shaping our technical direction—leading complex ML initiatives, elevating engineering standards, and guiding teams as they build scalable, production‑ready machine learning systems.
If you are ambitious, energized by solving challenging technical problems, passionate about developing talent, and excited to influence the future of ML/AI in the video game industry, this could be the perfect role for 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.
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 US 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 Data Scientist position, we will conduct a background check that may include the following:
Criminal history check
Employment verification
Education verification
Relevance to Job Responsibilities:
The background check is relevant to this position because of the following role responsibilities:
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.
Requirements:
Modeling Depth
Advanced Degree in Statistics, machine learning or related areas. Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions.
Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs. You should be able to walk through the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected.
Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks.
Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models).
Technology Familiarity
Supervised learning, transfer leaning on machine learning as well as neural networks
Basic LLM knowledge, especially how to use it and where not to use it.
MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines.
Model serving and inference optimization (vLLM-class serving, quantization).
Nice-to-Have:
Publications, conference talks, or recognized open-source contributions to training/eval tooling .
Graph-based fraud detection (fraud rings, device/account linkage)..
Gaming, payments, fraud, advertising domain experience.
Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work.
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