Description

About the Role In this role, you’ll be a part of the Data Science and Platform (DSP) team responsible for developing cutting-edge machine learning (ML) solutions in the fintech industry. Your contributions will have a significant impact on various customer touch points, such as account access, identity verification, transactions, ticket resolution, personalization, marketing, etc. The ideal candidate will demonstrate proficiency in data analysis, machine learning, and software engineering, while also possessing the ability to effectively collaborate with stakeholders who have diverse skill sets. You will focus on building and deploying real-time and batch ML models and systems helping Chime to navigate the ever-evolving landscape of the fintech industry. The base salary offered for this role and level of experience will begin at $133,300 and up to $185,100. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. In this role, you can expect to Design, train, and deploy state-of-the-art ML models to continuously enhance the efficacy of Chime's mission-critical machine learning systems. Identify the high impact business opportunities through data-driven solutions, and translate business problems into scientific formulations. Proactively seek internal tooling and standards opportunities to reduce ML model development time and deliver a fantastic DS experience. Help grow our SageMaker ML pipeline infrastructure for model training, evaluation, and deployment at scale. Drive innovation by building intricate feature engineering data pipelines that will significantly enhance the performance of our ML models. Proactively identify and develop ML techniques that can drive broad impact across multiple models, improving overall efficiency and effectiveness. Actively engage with the data community at Chime by sharing knowledge, suggesting process improvements, and promoting technical standards. Collaborate closely with data and platform engineers to design and build next-generation data/ML products to support Chime's data solutions at scale. To thrive in this role, you have Industry experience (consumer tech preferred) developing ML models from inception to business impact Strong programming skills (Python preferred) to develop ML & feature engineering systems and intermediate SQL skills to wrangle data from many disparate data sources Demonstrated expertise in developing and deploying advanced machine learning (XGBoost, CatBoost, LightGBM, etc.) and deep learning model frameworks, with a keen interest on optimizing data preparation, model training, and inference pipelines. Experience in working with advanced ML techniques such as embeddings, Graph Neural Networks (GNNs), transfer learning etc Proficient in leveraging various ML/DL toolkits, such as Keras, PyTorch, TensorFlow, Scikit-learn, SparkML etc. Familiarity with cloud and infrastructure services like Sagemaker, Airflow, Snowflake, Spark, Redis, Kafka, etc. Strong passion for teamwork and effective communication with both technical and non-technical stakeholders.

Role and Responsibilities

• In this role, you can expect to Design, train, and deploy state-of-the-art ML models to continuously enhance the efficacy of Chime's mission-critical machine learning systems. Identify the high impact business opportunities through data-driven solutions, and translate business problems into scientific formulations. Proactively seek internal tooling and standards opportunities to reduce ML model development time and deliver a fantastic DS experience. Help grow our SageMaker ML pipeline infrastructure for model training, evaluation, and deployment at scale. Drive innovation by building intricate feature engineering data pipelines that will significantly enhance the performance of our ML models. Proactively identify and develop ML techniques that can drive broad impact across multiple models, improving overall efficiency and effectiveness. Actively engage with the data community at Chime by sharing knowledge, suggesting process improvements, and promoting technical standards. Collaborate closely with data and platform engineers to design and build next-generation data/ML products to support Chime's data solutions at scale.