Lead Research Data Scientist

The Lead Research Data Scientist position is based onsite in Boston, MA, and sits within Epsilon's Decision Sciences IQ team. The role focuses on building automated segmentation, predictive modeling, and GenAI solutions for the PeopleCloud marketing SaaS, with responsibility for productionizing ML solutions and collaborating with engineering to embed them into the platform.

Responsibilities

• Lead projects from initial research through development in collaboration with stakeholders
• Develop familiarity with DSIQ's modeling platform and Epsilon's proprietary datasets
• Apply data science and machine learning expertise to investigate and propose optimal approaches for technology and business challenges
• Design, implement, and validate scalable analytic pipelines using tools such as Databricks, Spark, Python, MLlib, Keras, TensorFlow, Git, Jira, and Confluence
• Collaborate with Engineering teams to integrate solutions into Epsilon's PeopleCloud SaaS offerings
• Engage in the team's collaborative culture by sharing knowledge, debating techniques, and pursuing research to advance collective capabilities

Requirements

• Ph.D. in Computer Science, Statistics, Linguistics, Electrical Engineering, Mathematics, Economics, Physics, Operations Research, or a related scientific field
• Demonstrated research experience and coursework in Machine Learning
• At least 1 year of experience managing research and development projects
• At least 2 years of relevant programming experience gained through academic research, internships, or industry work
• Strong Python programming skills with experience in research or production code
• Experience working with large datasets
• Solid understanding of modeling and statistical techniques
• Desire to contribute in a highly collaborative environment
• Excellent communication and interpersonal abilities for conveying ideas and complex solutions

Technologies

• Databricks
• Spark
• Python
• MLlib
• Keras
• TensorFlow
• Git
• Jira
• Confluence
• Hadoop
• AWS
• LLMs
• Agentic frameworks

Additional Skills

• Experience with distributed and cloud computing platforms such as Hadoop, Spark, AWS, Databricks, or related technologies
• Experience with SQL and Python
• Familiarity with using LLMs and agentic frameworks
• Experience in Natural Language Processing, Information Retrieval, Mathematical Optimization, Control Theory, Time-Series Analysis, or Causal Inference