Lead, Machine Learning Engineer

Job Classification:
Technology - Data Analytics & Management

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability and efficiency? The Global Technology team takes great pride in our culture where digital transformation is built into our DNA! When you join our organization at Prudential, you’ll unlock an exciting and impactful career – all while growing your skills and advancing your profession at one of the world’s leading financial services institutions.

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services and experiences to delight our customers! In addition to advanced technical expertise and experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership attitude and a continuous learning focus to all that you do.

• Operationalize ML software models and components that solve real‑world business problems, while working in collaboration with the Product and Data Science teams; remove complex technical impediments.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Leverage cloud‑based architectures and technologies to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day‑to‑day.
• Work on complex problems in which analysis of situations or data requires an evaluation of intangible variables.
• Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL.

• Bachelor of Computer Science or Engineering or experience in related fields.
• Ability to coach others with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization.
• Experience with agile development methodologies and Test‑Driven Development (TDD).
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company’s business.
• Ability to learn new skills and knowledge on an on‑going basis through self‑initiative and tackling challenges.
• Excellent problem solving, communication and collaboration skills.
• Advanced experience and/or expertise with several of the following:
  • Software Engineering & System Design:
    Requirement analysis, coding, and testing, version control, microservices architecture, building Rest Ful APIs, Distributed computing, architecture patterns, general understanding of computer architecture, Object‑oriented programming concepts.
  • Machine Learning and Deep Learning:
    Good understanding of ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like Tensor Flow, PyTorch, scikit‑learn, Neural network, NLP, computer vision, and predictive analytics.
  • Model Performance Management: model monitoring, model validation, bias detection, explainability, performance, drift, outliers, etc.
  • Model Deployment:
    Thorough understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, Cloud Bees, Harness, etc.), A/B testing. Pipeline frameworks like MLFlow, AWS Sage Maker pipeline, etc. model and data versioning.
  • Data Integration, Transformation & Processing:
    Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL and graph databases,…