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JOB DESCRIPTION:

Global Finance Data Science Team at J&J is recruiting for a Machine Learning Engineer. This position is located in New Brunswick, NJ.
This role requires a good understanding of machine learning algorithms, strong software engineering skills, and the ability to translate business requirements into scalable machine learning solutions.
As a member of the Global Finance Data Science team, you will report to the Principal Machine Learning Engineer. You will be involved in designing, developing and deploying scalable and efficient Machine Learning models and systems on cloud platforms. You will be working closely with Data Scientists to understand technical requirements in Machine Learning projects and devising solutions to meet those needs.

TECHNICAL REQUIREMENTS

• Proficiency in Python, Bash and SQL with intermediate understanding of C/C++/Java/Scala/R.
• Basic familiarity with distributed computing frameworks such as Ray, Dask, and Spark.
• Adherence to software engineering best practices, including writing clean, maintainable, and efficient code.
• Basic understanding of CI/CD tools and related technologies, such as Jenkins, Bitbucket, Spinnaker and Helm.
• Strong team player with the ability to work effectively in collaborative, fast-paced, and high-achieving environments.
• Exceptional analytical and problem-solving skills.
• Good communication and presentation skills.
• Exposure to Deep learning frameworks such as PyTorch and TensorFlow.
• Familiarity with machine learning pipeline management and model monitoring tools (MLflow, Kubeflow, etc).

BASIC QUALIFICATIONS:

Required Minimum Education and Experience:

• Bachelor’s degree or higher in Computer Science, Engineering, or an equivalent qualification.

Preferred:

• AWS certification (e.g. Solutions Architect, DevOps Engineer
• Master’s degree in a relevant field.

The anticipated base pay range for this position is $106,250 to $143,750.

The Company maintains highly competitive, performance-based compensation programs. Under current guidelines, this position is eligible for an annual performance bonus in accordance with the terms of the applicable plan. The annual performance bonus is a cash bonus intended to provide an incentive to achieve annual targeted results by rewarding for individual and the corporation’s performance over a calendar/performance year. Bonuses are awarded at the Company’s discretion on an individual basis.

• Employees and/or eligible dependents may be eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance.
• Employees may be eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
• Employees are eligible for the following time off benefits:
• Vacation – up to 120 hours per calendar year
• Sick time - up to 40 hours per calendar year; for employees who reside in the State of Washington – up to 56 hours per calendar year
• Holiday pay, including Floating Holidays – up to 13 days per calendar year of Work, Personal and Family Time - up to 40 hours per calendar year
• Additional information can be found through the link below. https://www.careers.jnj.com/employee-benefits

The compensation and benefits information set forth in this posting applies to candidates hired in the United States. Candidates hired outside the United States will be eligible for compensation and benefits in accordance with their local market.

• Support the end-to-end development of machine learning models, from data collection and preprocessing to model training, evaluation, and deployment.
• Apply machine learning techniques and algorithms, such as deep learning, Generative AI, natural language processing, reinforcement learning, etc on finance datasets.
• Design and implement scalable machine learning algorithms and systems that can efficiently handle large volumes of data.
• Collaborate with Data Scientists, Data Engineers and business stakeholders to understand business requirements, translate them into technical requirements and deliver end-to-end data solutions.
• Develop robust, production-grade code for deploying machine learning models on cloud

platforms. Ensure solutions meet reliability, performance, and security standards.

• Implement best practices for model monitoring, performance optimization, and continuous integration/deployment.
• Research and stay updated with the latest developments and trends in machine learning and related fields and establish industry network by participating in internal and external forums, conferences, etc.