We are seeking a skilled machine learning engineer to develop and deploy advanced predictive models that analyze 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 high-quality data is used for model training and integration into production systems.
The role requires strong collaboration with key managers, product owners, and peers to drive business decisions and financial inclusion. You will create interpretable, accurate, and scalable predictive models using datasets from our AWS and Snowflake environments. Additionally, you will translate model insights into actionable strategies that inform business decisions.
Key responsibilities include coaching and mentoring fellow data analysts and engineers, ensuring efficient delivery through effective planning and prioritization, and developing, testing, and releasing your work. You will embody and foster a culture of excellence and innovation.
Core Competencies
* Proven expertise in programming, software development, and data analysis – ability to devise and implement solutions independently.
* Strong communication skills – ability to effectively translate technical concepts to non-technical audiences.
* High standards – delivering work of the highest quality and continuously raising the bar within the team and organization.
* Balance short-term goals with long-term vision – balancing speed with delivering the right outcomes.
* Passion for teaching and mentoring – inspiring and empowering others on your team.
* Prioritizing excellence and driving results – striving for exceptional outcomes and projects.
* Adaptability – thriving in a dynamic environment with new challenges.
Qualifications
* Mastery in mathematics, statistics, economics, computer science, or related quantitative fields. A relevant bachelor's degree with 10+ years of experience in machine learning and statistics may also be considered.
* Demonstrated expertise in AI and ML, particularly in the financial technology sector, including working with credit and financial datasets.
* A proven track record in building and deploying machine learning models, with a deep understanding of the underlying theory and trade-offs.
* Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression.
* Advanced knowledge of Python and SQL programming languages.
* Experience with cloud services, specifically AWS SageMaker and AWS Bedrock for model development, training, and deployment.
* Expertise in model registry tools, such as MLflow and SageMaker Model Registry, for managing machine learning models throughout their lifecycle.
* Familiarity with DevSecOps principles and implementing DataOps, MLOps, and DevSecOps in the AI and ML lifecycle.
* Experience with PyTorch, Scikit-learn, and GenAI models for building machine learning models.
* Knowledge of Large Language Models (LLM) frameworks, such as HuggingFace libraries, and agent-based frameworks like LangChain and Mirascope.
* Familiarity with Snowflake for cloud data warehousing and efficient handling of large-scale data storage and processing.
Beyond the Basics
* PhD in mathematics, statistics, economics, computer science, or related quantitative fields.
* Experience working in FinTech or PropTech sectors.
* Familiarity with startup environments and microservices architecture.
* Compliance knowledge and experience working in SOC2-certified or ISO-certified organizations.
Your Benefits
* Competitive salary and comprehensive benefits package.
* Remote-first work environment that empowers you to achieve your best.