Description
Chime's Data Science & Machine Learning team builds the models, services, and platforms behind how millions of members manage and grow their financial lives.
In this role, you'll build and deploy machine learning systems on some of the richest transactional and behavioral data in fintech, turning it into decisions that protect members from fraud, expand access to credit, and power the personalized experiences and marketing that help millions of members get more out of products.
Responsibilities
- Build, train, and deploy deep learning and classical ML models on large-scale financial, transactional, and behavioral datasets
- Take models from problem framing through to production , training, evaluation, deployment, monitoring, and iteration , and stay accountable for how they behave once they're live
- Design and improve the systems around the model: feature pipelines, batch and real-time inference, monitoring, and retraining
- Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into ML solutions, and to make sure the solution is the right one
- Connect model performance to member outcomes and business metrics, and use experimentation to prove impact
- Contribute to the shared ML platform, tooling, and standards that the rest of the team builds on
- Help identify where AI/ML creates measurable impact for members , and where a simpler answer is the better one
Requirements
- Experience building and deploying deep learning models in production, with a solid grasp of architecture choice, training dynamics, and evaluation , and the judgment to model design choices
- Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you
- Hands-on experience across the end-to-end ML lifecycle , training, experimentation, optimization, deployment, and monitoring
- Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew
- Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark
- Working knowledge of modern ML infrastructure , AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake , and an MLOps mindset for keeping production systems healthy
- The ability to operate independently in ambiguous environments, and to communicate clearly with both technical and non-technical partners
Nice-to-Have
- Experience in any one of these is a plus, and helps us match you to the right team: Trust & Safety , fraud, risk, abuse detection, or adversarial modeling; Lending , credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements; Growth , personalization, recommendation, marketing measurement, or experimentation at scale; Foundation Models , transformers on tabular or semi-structured data, large-scale pretraining, ML platform work, or applied research with a publication record
Benefits
- Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location
- Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees
- Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life
- Generous vacation policy and company-wide paid days off
- 1% of your time off to support local community organizations of your choice
- Annual wellness stipend to use towards eligible wellness related expenses
- Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents
- Access to family planning reimbursement