Equifax is excited to add a Machine Learning Engineer to our team.
What You’ll Do
- Design complex systems of systems for training and running machine learning models with industry best practice
- Define projects and scope for teams of engineers and guide their completion
- Develop, identify, and report intellectual property through patent applications, invention disclosures, white papers, and presentations
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including executives, customers, and peers from other businesses and institutions
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment
- Deliver on company initiatives and prioritize projects supporting your long term technical vision
- Collaborate with the product team, architects, and others to understand the opportunities and limitations of AI, ML, and data engineering
- Participate in peer design and code reviews
- Show initiative to identify and drive forward improvements and innovations that add value and move the IT organization forward
- Elevate the performance of colleagues through training, mentoring, and promoting best practices; may function as a team lead
What Experience You Need
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 7+ years of related work experience, including proven experience leading a team of MLE, DS, SDE, DevOps, or related roles
- Experience with end-to-end development of ML models, from ideation to deployment, ensuring best practices, scalability and reliability
- Cloud Certification Strongly Preferred
What Could Set You Apart
- Application Development/Programming - Ability to review code for quality, performance, and efficiency, and optimize critical parts of the codebase; Ability to establish the best practices of Software Development Life Cycle for the team
- Artificial Intelligence - Designing scalable and maintainable machine learning architectures and frameworks for the organization's products and services; Ability to define the technical vision and roadmap for the MLE team aligned with the organization's goals and industry trends
- Big Data Analytics - Deep understanding of the domain or industry in which the machine learning solutions are being applied, enabling the company to develop impactful big data solutions
- Cloud Computing - Proficiency in data architecture design, data strategy development, data orchestration, data integration, ETL development, data modeling, parallel processing and performance optimization.
- Collaboration - Being able to engage with internal stakeholders, including data scientists, business leaders, product managers, and executives, to understand requirements and present technical solutions; Ability to collaborate with other teams, such as software engineering, data engineering, and business intelligence, to integrate machine learning solutions into larger systems.
- Mathematics - Ability to read and comprehend research papers in latest machine learning field, and applying innovative techniques to real-world problems
- Technical Leadership - Be able to lead and manage a team of machine learning engineers, data scientists, or related roles. Ability to set clear goals, provide guidance, and foster a collaborative and productive team environment