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Machine Learning Engineer - RCS Analytics (ML, Experimentation)

CVS Health

$86,520 - $173,040
Apr 15, 2025
New York, NY, US
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Revolutionize customer interactions by enhancing traditional SMS/MMS communications through sophisticated customer journeys and audience designs leveraging predictive analytics.

Requirements

  • 2+ years of experience with data preprocessing, feature engineering, and model evaluation techniques
  • 2+ years of experience in building end-to-end machine learning models and data-intensive systems in production
  • 2+ years of strong background in Data Science with a focus on software development best practices
  • 2+ years of proficiency in Python, SQL, and MLOps and experience working with and/or developing APIs
  • 2+ years of experience in cloud environments
  • Demonstrated ability to design and build distributed data-intensive products including Front End and web app development
  • Experience with Snowflake or GCP

Responsibilities

  • Build and facilitate statistical and machine learning solutions across large-scale systems and campaigns
  • Develop and participate in presentations and consultations to existing and prospective stakeholders on analytics results and solutions
  • Collaborate with internal and external peers and managers to exchange complex information related to areas of specialization
  • Design and execute A/B experiments and perform statistical analyses
  • Develop models for audience design
  • Onboard to new tools for executing RCS tactics and share learnings back to the team
  • Work closely with data engineering and MLOps to ensure scalable and sustainable solutions

Other

  • Bachelor's degree or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline
  • Master's degree preferred
  • 40 anticipated weekly hours
  • Full-time position
  • Hybrid schedule in New York City office
  • Affordable medical plan options, 401(k) plan, employee stock purchase plan, no-cost wellness programs, paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access