Job Information
Prudential Ins Co of America Vice President, Principal Machine Learning Engineer in Newark, New Jersey
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, youll unlock an exciting and impactful career all while growing your skills and advancing your profession at one of the worlds leading financial services institutions.
As a VP, Principal 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 deep 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.
You will set technical standards and bring deep technical expertise and experience within 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 being a technical expert, 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.
Here is what you can expect in a typical day:
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 and align the roadmap.
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 Expert understanding of relevant and emerging technologies, provide technical guidance and leadership to team members and role-model our continuous learning culture and embed learning and innovation in the day-to-day
Work on critical applications, maintain a broad knowledge of innovative principles and theory
Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL
The Skills and expertise you bring:
Bachelor of Computer Science or Engineering or experience in related fields
Lead 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
Significant experience and/or deep expertise with several of the following:
Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul 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 TensorFlow, PyTorch, scikit-learn etc., 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, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker 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, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark
Statistics and Computing: Strong knowledge of: Linear Algebra, Probability and Statistics, Multivariate Calculus, Distributions like Poisson, Normal, Binomial etc.
Programming Languages: Python, R, SQL, Java or Scala, SQL
What we offer you:
Market competitive base salaries, with a yearly bonus potential at every level
Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave
Retirement plans:
401(k) plan with company match (up to 4%)
Company-funded pension plan
Wellness Programsto help you achieve your wellbeing goals, including up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs
Work/Life...
Equal Opportunity Employer - minorities/females/veterans/individuals with disabilities/sexual orientation/gender identity