Staff AI/ML Applied Engineer

Summary

Join Zipdev as an ML/AI Applied Engineer and apply advanced machine learning models to predict key financial factors like creditworthiness and cashflow. Collaborate with Data Analytics and Data Engineering teams to ensure model quality and production integration. This role demands strong communication and collaboration skills, working with various teams across the organization. You will create interpretable, accurate, and scalable predictive models using data from AWS and Snowflake environments, translating model insights into actionable business strategies. Mentoring fellow data analysts and engineers is also a key aspect of this position. The ideal candidate will have a strong background in machine learning, experience with relevant technologies, and a passion for building and deploying high-quality models.

Requirements

• Master’s degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis
• Relevant bachelor’s degree in a STEM field with 10+ years of relevant work experience in Machine Learning and Statistics
• Strong experience in AI and ML in the financial technology or service industry, including working with credit and financial datasets
• 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
• Very strong knowledge of Python and SQL
• Strong 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
• 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
• Experience in 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 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

Responsibilities

• Apply advanced machine learning models that predict and model creditworthiness, transaction labeling, identity mapping, underwriting, cashflow, lease renewals, and other key financial factors
• Work closely with the Data Analytics and Data Engineering teams to ensure the models are trained on high-quality data and integrated into production systems
• Collaborate with key managers, product owners, and other peers and cross-functional partners
• Create interpretable, accurate, and scalable predictive models utilizing datasets generated from our AWS and Snowflake environments
• Translate model insights into actionable strategies that drive business decisions and financial inclusion
• Coach and mentor your fellow data analysts and data engineers
• Ensure efficient delivery through effective planning, engaging with others, prioritizing, and developing, testing, and releasing your work

Preferred Qualifications

• PhD degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis
• Experience in the FinTech or PropTech areas
• Experience in a startup environment
• Experience with microservices
• Experience working with data governance policies in SOC2 or similarly certified organizations

Benefits

• Work remotely Monday - Friday, 40 hours a week (no weekends)
• Vacation: 10 business days a year
• Holidays: 5 National Holidays a year
• Company Holidays: 5 Company Holidays a year (Christmas Eve, Christmas Day, New Year's Eve, New Year's Day, Zipdev Day)
• Parental Leave
• Health Care Reimbursement
• Active Lifestyle Reimbursement
• Quarterly Home Office Reimbursement
• Payroll Deduction Purchase Plans
• Longevity Bonus
• Continuous Learning Bonus
• Access to Training and Professional Development Platforms