Data Scientist

8 Hours ago • 3 Years + • Data Analyst • Product Management

Job Summary

Job Description

The Data Scientist at gTech Scaled Technical Solutions will lead data science aspects of client engagements focused on marketing portfolio management. Responsibilities include collaborating with customers to identify optimal statistical techniques, developing modeling frameworks, assessing data readiness, and scaling proof-of-concept solutions. The role also involves translating data and model results into actionable insights, integrating recommendations into business processes, and collaborating with Product/Engineering teams to optimize Applied DS team capabilities. The ideal candidate will have experience using analytics to solve business problems, coding in Python, R, or SQL, and experience with ML and marketing analytics.
Must have:
  • Master's degree in quantitative discipline or equivalent experience
  • 3+ years experience using analytics to solve problems
  • Proficiency in Python, R, SQL
  • Experience with statistical analysis and ML
  • Lead data science aspects of client engagements
Good to have:
  • Experience delivering ML insights to customers
  • Experience with digital analytics and measurement solutions
  • Experience in Computer Vision and NLP in marketing analytics
  • Knowledge of statistical algorithms in Marketing Analytics
  • Ability to utilize generative AI in marketing

Job Details

Minimum qualifications:

  • Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  • 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

Preferred qualifications:

  • Experience delivering insights from ML to customers (e.g., problem scoping/definition, modeling, interpretation).
  • Experience using or deploying digital analytics and measurement solutions.
  • Experience in Computer Vision and NLP in the context of marketing analytics, with the ability to bring generative AI technologies to customer problems in marketing.
  • Knowledge of the statistical algorithms used in Marketing Analytics.

About the job

gTech Ads is responsible for all support and media and technical services for customers big and small across our entire Ad products stack. We help our customers get the most out of our Ad and Publisher products and guide them when they need help. We provide a range of services from enabling better self help and in-product support, to providing better support through interactions, setting up accounts and implementing ad campaigns, and providing media solutions for customers business and marketing needs and providing complex technical and measurement solutions along with consultative support for our large customers. These solutions range from bespoke and customized ones for our customers to scalable support for millions of customers worldwide. Based on the evolving needs of our ads customers, we partner with Sales, Product and Engineering teams within Google to develop better solutions, tools, and services to improve our products and enhance our client experience. As a cross-functional and global team, we ensure our customers get the best return on investment with Google and we remain a trusted partner.

The gTech Scaled Technical Solutions team is a group of engineers and technical experts, within the gTech Ads Solutions organization, developing customer focused solutions and platforms - increasing scale, reach, and team velocity for all deliverables to customers, sellers, and partners. Solutions launched have the ability to impact thousands of customers and influence future product road map, built around technical best practices and leading edge technology.

Responsibilities

  • Lead data science aspects of client engagements in the area of marketing portfolio management.
  • Collaborate with customers to solve their problems and identify the best statistical techniques that can solve the problem, and own the development of modeling framework.
  • Engage important stakeholders to assess data and model readiness and be able to scale a proof-of-concept to a solution.
  • Work with customer and internal teams to translate data and model results into tactical and strategic insights for decision-making, and work with clients to integrate recommendations into business processes.
  • Collaborate with Product/Engineering teams to increase and optimize capabilities of the Applied DS team, employing methods which create opportunities for scale, and help to drive innovation.

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