Data Science Manager, gTech Ads Solutions

42 Minutes ago • 7-10 Years • Data Analyst

Job Summary

Job Description

As a Data Science Manager at Google's gTech Ads Solutions, you'll lead a team of data scientists using Google's Machine Learning capabilities to create data science and measurement solutions for large customer sales (LCS) clients. You'll collaborate with clients, agencies, and internal teams, applying best practices to solve marketing challenges. Responsibilities include developing a data science roadmap, setting project goals, providing technical leadership, and offering analytical thought leadership. You'll also collaborate with product and engineering teams to optimize capabilities and drive innovation. The role requires strong leadership, communication, and technical expertise in data science and marketing.
Must have:
  • Master's degree in quantitative discipline
  • 7+ years analytics experience
  • 3+ years people management
  • Coding proficiency (Python, R, SQL)
  • Lead data science teams
  • Deliver data science solutions
Good to have:
  • Experience presenting to executives
  • Experience with data science/engineering team evolution
  • Strong cross-functional collaboration skills
  • Excellent communication skills

Job Details

Minimum qualifications:

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

Preferred qualifications:

  • Experience preparing and delivering presentations to executive leadership.
  • Experience in leading and evolving data science and engineering teams with technical strengths.
  • Ability to adept at distilling analyses into data driven narratives.
  • Ability to operate in a cross-functional organization and collaborate with other teams.
  • Excellent communication skills, with the ability to communicate technical and statistical concepts clearly and concisely among audiences at different levels.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

As a Data Science Manager, you will lead a team of technical experts, utilizing Google’s Machine Learning capabilities to build data science and measurement solutions to Large Customer Sales (LCS) clients. You will collaborate with clients, agencies, and internal teams.

In this role, you will be responsible for leading a team of data scientists in applying best data science practices to solve ads customers’ problems in marketing. You will be responsible for building and executing gTech’s strategy and outlook to deliver applied data science to Google's ads customers, utilizing Google’s Machine Learning capabilities to build innovative solutions that address the customer's problem-space.

Responsibilities

  • Lead a team of Data Scientists responsible for delivering data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management.
  • Develop a data science roadmap for the team that aligns with Data Science strategy and OKRs, establish project goals, align individuals' expectations, coordinate resources, and provide technical leadership with the long-term objectives.
  • Provide analytical thought leadership in the areas of Data Science and Measurement through proactive and strategic contributions.
  • Influence stakeholders through presentations and written communication.
  • Collaborate with Product/Engineering teams to increase and optimize capabilities, employing methods which create opportunities for scale, and help to drive innovation.

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