Prudential Financial · 4 days ago
Director, Machine Learning Engineer
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Asset ManagementFinance
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Responsibilities
Operationalize ML software models and components to solve real-world business problems
Write and test application code, develop and validate ML models, and automate tests and deployment
Leverage cloud-based architectures to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Implement continuous integration and deployment best practices for successful deployment of ML models
Provide technical direction to team members, embed learning and innovation in day-to-day operations
Work on significant and unique issues requiring evaluation of intangible variables
Utilize programming languages like Python, R, SQL, Java, or Scala
Qualification
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Required
Bachelor of Computer Science or Engineering or experience in related fields
Ability to lead independently 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 ongoing 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
Benefits
Medical
Dental
Vision insurance
Life insurance
Disability insurance
Paid Time Off (PTO)
Parental leave
Military leave
401(k) plan with company match
Company-funded pension plan
Wellness Programs
Tuition Assistance
Employee Stock Purchase Plan
Company
Prudential Financial
Prudential Financial specializes in the fields of investment management, life insurance, and retirement benefits. It is a sub-organization of Prudential Financial.
Funding
Current Stage
Public CompanyTotal Funding
$500M2024-02-14Post Ipo Debt· $500M
2021-07-21Acquired· by Empower Retirement ($3.54B)
2001-12-21IPO· nyse:PRU
Leadership Team
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