Data Scientist
Ondo FinanceFull Time
Junior (1 to 2 years)
Candidates must be fluent in English and possess programming proficiency in Python and SQL. A minimum of 1 year of experience in a data role analyzing media and web data from platforms like Amazon Marketing Cloud, Google ADH, Campaign Manager, Meta, and Google Analytics is required. Proficiency in data visualization tools such as Tableau, Looker, or Matplotlib, and strong experience with the marketing ecosystem and measurement frameworks including paid display, search, social, video, organic, web, and app are essential. Skilled in media measurement methodologies like media mix modeling, incrementality analysis, and attribution modeling, along with an understanding of machine learning concepts and hands-on experience, are necessary. A collaborative mindset, problem-solving skills, and a proactive approach to improving and automating processes are also required. While traditional degrees and years of experience are not strictly emphasized, relevant skills, expertise, and behavioral attributes are highly valued.
The Data Scientist Executive will analyze data to uncover trends and deliver actionable recommendations. They will explain and promote the use of data science methodologies such as Media Mix Models, Geo-based Incrementality, Experiments, and Causal Impact Studies to enhance campaign performance for clients. Responsibilities include developing and implementing Marketing Mix Models to improve media performance, customer experiences, and revenue generation. The role involves working with platform partners to advance mathematical techniques for Incrementality Testing, Media Mix Modeling, and Data Clean Rooms using both open-source and in-house code. Additionally, the executive will document analytics projects to ensure processes can be replicated and scaled across the organization.
Platform for engineering intelligence and strategy
Jellyfish offers a platform that integrates business and financial strategies into the Software Development Life Cycle (SDLC). It provides insights into engineering efforts, helping businesses improve planning and decision-making while identifying bottlenecks to enhance operational efficiency. The platform also tracks deliverable progress and focuses on team health by combining quantitative data with qualitative feedback. Jellyfish aims to improve the effectiveness of engineering teams, ensuring they can deliver value efficiently.