Job Opportunity: Senior Full Stack Engineer
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The role of a Machine Learning (ML) Engineer involves developing and deploying advanced machine learning models to predict creditworthiness, transaction labeling, identity mapping, underwriting, cash flow, lease renewals, and other key financial factors. This position will collaborate closely with the Data Analytics and Data Engineering teams to ensure that the models are trained on high-quality data and integrated into production systems.
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This ML Engineer will focus on creating interpretable, accurate, and scalable predictive models using datasets generated from our cloud environments. Additionally, they will translate model insights into actionable strategies that drive business decisions and financial inclusion.
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In addition to technical responsibilities, this role involves coaching and mentoring fellow data analysts and engineers. The individual must ensure efficient delivery through effective planning, engagement with others, prioritization, and development, testing, and release of their work.
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Key Responsibilities
* Develop and deploy advanced machine learning models to predict creditworthiness, transaction labeling, identity mapping, underwriting, cash flow, lease renewals, and other key financial factors
* Collaborate with the Data Analytics and Data Engineering teams to ensure that the models are trained on high-quality data and integrated into production systems
* Create interpretable, accurate, and scalable predictive models using datasets generated from our cloud environments
* Translate model insights into actionable strategies that drive business decisions and financial inclusion
* Coach and mentor fellow data analysts and engineers to ensure efficient delivery through effective planning, engagement with others, prioritization, and development, testing, and release of their work
Requirements
* Master's degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis
* Strong experience in AI and ML in the financial technology or service industry, including working with credit and financial datasets
* Proven track record in building and deploying machine learning models, with a strong understanding of the theory and tradeoffs behind these techniques
* Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression
* Strong knowledge of Python and SQL
* Experience with AWS cloud services and tools, including AWS SageMaker for model development, training, and deployment, and AWS Bedrock for building and fine-tuning foundation models
* Experience working with model registry tools such as MLflow, SageMaker Model Registry, or other similar systems, to track, version, and manage machine learning models throughout their lifecycle
* Experience implementing DataOps, MLOps, and/or DevSecOps in the AI, ML, and software development lifecycle
* Experience building ML models with PyTorch, Scikit-learn, and GenAI models
* Experience working with LLM frameworks such as HuggingFace libraries and with agent-based frameworks such as LangChain and Mirascope
* Familiarity with Snowflake for cloud data warehousing, data integration, and efficient handling of large-scale data storage and processing
Desired Qualifications
* PhD degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis
* Experience working in FinTech or PropTech sectors
* Familiarity with the dynamics of startup environments
* Experience with microservices
* Experience working in SOC2-certified, ISO-certified, or similar organizations
Benefits
* Competitive Salary
* Remote first work environment