تم وضع المناقشة في: غيرها في شيكاغو | علق: |
Job Description
In this role, you will contribute to research initiatives and develop enhancements to Epsilon’s CORE Personalization Platform to continually increase the capabilities of Epsilon’s digital marketing businesses.
We prioritize analytical thinking, creativity and computer science skills over ad tech industry experience. If using internet-scale data to drive business impact excites you, and you enjoy the kind of research that starts with a blank sheet of paper that turns ideas into a new technological reality, we want to talk to you.
Responsibilities
Individually contribute to data science and machine learning R&D projects
Use your 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 state-of-the-art algorithms in distributed environments
Develop an understanding of Epsilon PeopleCloud Digital Media Solutions personalization platform and proprietary datasets
Participate fully in our collaborative approach to research and applications projects
Qualifications
Ph.D. in a computational, mathematical, engineering, or scientific field
Fluency in computer programming, data structures, and algorithm design
Proven analytic and modeling skills
Proven ability to conduct research projects
Research experience in a computational area such as data science, computer science, machine learning, artificial intelligence, statistics, or graph algorithms
Verbal and written communication skills, including the ability to summarize technically complex information for a non-technical audience
Organizational, motivational, and interpersonal skills
Additional, But Not Required Skills
Spark libraries
Scala programming
Python programming
SQL queries
Experience with distributed computing
Experience with Hadoop and cloud databases
Demonstrated proficiency working with business and technical teams to integrate algorithms into product platforms on large data sets