iSpot
DataScientist
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Scientist at iSpot”
Industry & Context.
Data-driven decision making
Full Job Description
Immigration / Work Authorization Notice: Applicants must be currently authorized to work in the United States. iSpot is not able to sponsor or take over sponsorship of an employment visa for this position at this time. iSpot competes for the best talent. Our compensation packages consist of salary and equity in one of Seattle’s hottest start-ups, as well as other standard benefits. Most importantly, we provide a really interesting working experience, and the chance to contribute to the success of something great. About iSpot: At iSpot, we empower advertisers to measure the true impact of their TV and streaming campaigns—from concept through airing to conversion. Our fast, accurate, and actionable measurement and attribution solutions enable advertisers to evaluate creative effectiveness, optimize media plans, and attribute results across platforms, all while benchmarking against competitors and historical data. With real-time performance insights seamlessly integrated across linear and streaming TV, iSpot equips advertisers to make swift, confident decisions that drive impactful business results. About the Role: We are seeking a Data Scientist to join our team. In this role, you will analyze, manipulate, clean, and process large and messy data using predictive modeling solutions and cloud-based computing resources/infrastructure to scale and optimize data science tools and pipelines. Responsibilities: You will be responsible for a variety of critical tasks, including: Implement industry-standard predictive modeling solutions. Analyze, manipulate, clean, and process large and messy data using predictive modeling solutions and cloud-based computing resources/infrastructure to scale and optimize data science tools and pipelines. Code original R/Python scripts, optimize current scripts, and develop analytical reports and models. Analyze and improve data methods and efficiencies across the team, including designing and implementing wrapper/tool/utility functions for auto
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