Machine Learning Engineer - (Python, Credit Risk) Jobs in USA, NJ, Rutherford | Rose International Job

Required Education:

• Bachelor's degree/University degree or equivalent experience in

STEM Preferred Education:

• PhD or master's degree in computer science, Data Science, Statistics, Mathematics, Engineering or related field

Required Qualifications/Skills/Experience:

• 8-10+ years of industry experience as a data scientist, specializing in ML Modeling, Ranking, Recommendations, or Personalization systems
• 8-10+ years of experience designing and developing scalable and reliable machine learning systems for training, inference, monitoring, and iteration
• Strong background of

ML/DL/LLM

• Proficiency in Python, SQL, Spark, PySpark, TensorFlow or other analytical/model-building programming languages
• Proficiency with tools and LLMs
• Ability to work independently and collaboratively within a team

Preferred Qualifications/Skills/Experience:

• Experience in GenAI/LLMs projects
• Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL)
• Background in financial business, like banking, risk management
• Should be familiar with capital markets, financial instruments and modeling

Overview:

• This is a development position for establishing and implementing new or revised applications and programs in the Technology team
• As part of this role, you will be responsible for data extraction and data analysis from structured and unstructured sources
• You will develop systems to clean results, build predictive and prescriptive models and implement them in a production environment by partnering with technology and business partners
• You will have to address complex problems involving financial data with a specific focus on credit risk management
• You will have to have an open and adaptive mindset to learn new and advanced models in LLM and GenAI and bring in innovative solutions to complex business problems

Job Duties:

• Lead and develop plans and coordinate with teams for all analytical efforts
• Manage deliverables in an agile environment and maintain clear communication with all model stakeholders
• Present status, issues, and analytical findings to various audience groups like business, technology management, risk review, model governance, etc.
• Data modelling and cleaning from internal and external sources
• Build predictive and prescriptive models by manipulating and cleaning results
• Lead, develop, manage, and deploy analytical solutions using Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) to production systems using the SDLC process
• Implement features through the ML lifecycle (Development, Testing, Training, Production, Monitoring/Evaluation) to ensure scalability and reliability
• Only those lawfully authorized to work in the designated country associated with the position will be considered.
• Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client's business needs and requirements.