Asst Dir-Machine Learning Eng

Skills and Competencies 

• Excellent written and verbal communication skills, with the ability to understand and articulate business requirements and objectives to both technical and non-technical stakeholders. 

• Expertise in supervised and unsupervised machine learning algorithms and their implementations, machine learning concepts including - besides the usual - e.g. active learning, computer vision, and deployments in complex setups.  

• Expertise in Python programming including ML relevant libraries like numpy, pandas, pytorch. 

• Experience in machine learning operations (ML Ops) practices, including CI/CD pipelines for ML models, model monitoring, and maintenance is a plus. 

• Expertise with tools in the modern ML stack such as Jupyter, Docker, Git and cloud computing on AWS or GCP. 

• Expertise in building data tools for ETL, extracting data from SQL and NoSQL databases, and data analysis.  

• Experience with GIS systems is preferred.  

• Understand our core values & guiding principles; make them the guideline for how you do your job.  

• Take Initiative - be enthusiastic learners and advocates of our technology, products and services. Demonstrate an owner mindset while at CAPE.  

• Create Impact - spend time learning what it takes to provide value to internal and external stakeholders. We measure our success not by what we build, but by how much value it creates for others.  

• Contribute to a High-Candor environment - deliver and invite candid feedback, and be a part of the solution.  

• Get, give, and dig for Context - share what you know and seek to expose the complete picture  

• Win as a team - make things happen by working together, being open to new ideas, and fostering structured team communication. Avoid silos of expertise.  

• Respect and promote inclusion & diversity.  

• Do what's right for each other, our clients, investors and our communities. 

 
Education  

Successful candidates will typically have a PhD degree in a STEM field, a master's degree in a STEM field with 5+ years of hands-on industry experience, or a bachelor's degree in a STEM field with 7+ years of hands-on industry experience. However, we also solicit applications from candidates with comparable, albeit less formal, backgrounds. 

Responsibilities 

You will work independently in a small team of highly skilled - and lovely - individuals, building practical, robust, and scalable machine learning solutions. You will be responsible for improving and extending our core product offering and contribute to product research, meeting high standards while delivering maximum value to our clients

Data Exploration:  

• You collect, clean, and preprocess data for data analysis and machine learning and maximize the value of that data. 

• You conduct data analysis to understand distributions, patterns, and issues.  

• You create visual representations of data to make complex findings more understandable. 

• You identify and explore new and existing data sources to optimize and validate our models. 

• You leverage data from internal and external sources to generate structured knowledge about our space. 

Model Development and Improvement: 

• You train and refine machine learning models based on business objectives. 

• You improve model performance by identifying and correcting failure modes.  

• You support us in maintaining a robust and secure computing environment for machine learning applications in coordination with DevOps and IT. 

• You contribute to the design, implementation, and automation of large-scale model training, model integration, and evaluation pipelines. 

• You consider the ethical implications of data use to ensure responsible and fair practices. 

• You design and conduct experiments to test hypotheses, validate assumptions, and draw relevant conclusions. 

• You make a pragmatic choice of the appropriate machine learning algorithms based on the characteristics of the data, the problem, and the business constraints. 

• You ensure that the projects you lead follow our governance processes. 

Coordination, Planning, and Communication:  

• You collaborate with cross-functional teams, including CAPE by Moody’s machine learning, software engineering, product development, and sales teams. 

• You communicate technical findings to non-technical stakeholders. 

• You contribute to the knowledge base and ongoing skill transfer throughout the engineering organization. 

• You defend your methodology and incorporate feedback from internal teams and clients. 

• You participate in project planning and prioritization discussions. 

• You think holistically about inefficiencies and take ownership in resolving them. 

• You objectively weigh the pros and cons of changes to the design or implementation of our systems, demonstrating a balanced understanding of the implications of these decisions. 

• You contribute to quarterly planning by proposing projects and reviewing others' ideas. 

• You document complex findings in an easily accessible manner suitable for technical and non-technical audiences. 

• Other duties and responsibilities as assigned.  

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