D. in Computer Science, Electrical Engineering, Statistics, Mathematics, Economics, Physics, Chemistry, Operations Research, or related quantitative field Research experience and coursework in Machine Learning Strong understanding of statistical, analytical, and predictive modeling techniques Strong Python programming skills and experience with data structures, algorithms, and production-quality code development 2+ years of relevant programming experience through academic research, internships, or industry work Ability to code independently without AI as well as effectively leverage AI-assisted development tools Proven ability to design, execute, and deliver research projects Excellent communication and interpersonal skills, including ability to translate complex technical concepts for non-technical audiences Self-motivated, highly organized, and collaborative, with the ability to manage multiple priorities in highly collaborative, team-oriented environments Additional, But Not Required Skills Experience with graphs/networks and unsupervised clustering algorithms (community detection on graphs) Experience working with large, complex datasets in research or production environments Experience with distributed computing and large-scale data processing (Spark, Hadoop, Databricks, cloud databases) Experience with cloud platforms and data ecosystems (AWS, S3, Databricks) Proficiency in SQL, Scala, Spark libraries, and advanced database querying Familiarity with LLMs, Generative AI, and agentic frameworks Experience in one or more advanced domains: Community Detection on Graphs, Unsupervised Learning, NLP, Information Retrieval, Mathematical Optimization, Control Theory, Time-Series Analysis, or Causal Inference Ability to partner with business and technical collaborators to deploy algorithms into production platforms Experience translating research innovations into scalable, customer-facing products Additional research experience in computational fields such as data science, AI/ML, statistics, computer science, or graph algorithms Click here to view how Epsilon transforms marketing with 1 View, 1 Vision, 1 Voice. Responsibilities Individually contribute to data science and machine learning R&D projects Use data science, machine learning, and/or computer science skills to conduct research and contribute to solutions to technology and business problems Contribute to projects from early-stage research through development Implement and optimize innovative algorithms in distributed environments Develop an understanding of Epsilon personalization platform and proprietary datasets Participate fully in our collaborative approach to research and applications projects Qualifications Qualifications Ph.