Machine Learning Engineer Position
This role involves applying advanced machine learning models to predict and model creditworthiness, transaction labeling, identity mapping, underwriting, cash flow, lease renewals, and other key financial factors. The successful candidate will 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.
Key Responsibilities:
* Design and develop interpretable, accurate, and scalable predictive models utilizing datasets generated from our cloud environments.
* Translate model insights into actionable strategies that drive business decisions and financial inclusion.
* Cocoon and mentor fellow data analysts and engineers to promote a culture of collaboration and knowledge-sharing.
Core Competencies:
1. Superb programming, software, and data development skills.
2. Strong communication skills, including efficient translation between technical and non-technical audiences.
3. High standards of quality and excellence in all aspects of work.
4. Balance thinking big with delivering the right thing in an agile and speedy manner.
5. Able to inspire and empower others through effective coaching and mentoring.
6. Prioritize delivering high-quality outcomes and projects while striving for excellence.
7. Adaptable and flexible in a rapidly changing environment.
Basis Requirements:
* Master's degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis.
* Very 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.
* Very strong knowledge of Python and SQL programming languages.
* Strong experience with AWS cloud services and tools, including SageMaker for model development, training, and deployment.
* Experience working with model registry tools such as MLflow, SageMaker Model Registry, or other similar systems.
* DataOps, MLOps, and/or DevSecOps implementation experience in the AI, ML, and software development lifecycle.
* Building ML models with PyTorch, Scikit-learn, and GenAI models.
* Familiarity with Snowflake for cloud data warehousing, data integration, and efficient handling of large-scale data storage and processing.
Additional Desirable Qualifications:
* PhD degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis.
* FinTech/PropTech expertise.
* Startup experience.
* Software engineering experience with microservices.
* Compliance knowledge in SOC2-certified or ISO-certified organizations.
About Our Offerings:
* Competitive salary package.
* Remote first work environment where we trust you to get your work done.