Senior Data Scientist - Ad Intelligence

September 6

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Sensor Tower

WebsiteLinkedInAll Job Openings

mobile analytics • mobile data • market intelligence • business intelligence • app store optimization

201 - 500

💰 $45M Private Equity Round on 2020-05

Description

• Execute on the development of data modeling algorithms with a focus on: analyzing large sets of data, building models, ensuring data accuracy, reliability and consistency. • Develop, refine and bring data models to production. This role will work on models that are consumed by our end users, building internal tools and one time reports as well as working on the actual product that our users consume and rely on every day. • Consult on the use of AI to improve workflows for data extraction and classification. • Embrace a quick build, measure and iterate cycle to bring products to market and work alongside world class engineers, and data scientists. • Act as subject matter expert for our digital advertising.

Requirements

• Degree in Mathematics/ Statistics/ Computer Science or a related engineering/ technical or quantitative field. • Min 4 years of experience as a Data Scientist in the ad-tech / mar-tech or data analytics companies, including the following experience in: • business intelligence, data mining, analytics, and statistical modeling disciplines; • analyzing and presenting data insights and code, communicating effectively with technical developers and non-technical marketing business partners, teammates and leadership; • implementing machine learning algorithms, working end-to-end on machine learning pipelines in production; • data engineering working on ETL pipelines, crawling APIs and websites, and automating outputs (report generation, workflow automation, Google Sheet interaction); • setting and meeting detailed timelines and expectations while executing projects, developing projects in a resilient way that anticipates future changes and interaction with other parts of the product; • data research, identifying the necessary edge cases that need to be tested in order to fully understand the data; • programming in Python or Ruby, utilizing AWS S3, MongoDB, PostgreSQL, AWS Redshift or similar database technologies; • using Jupyter notebooks and one or more statistical visualization or graphing toolkits such as Excel, Qlik Sense or Tableau.