Applied AI​/ML Business Analytics Lead

Google place Mountain View, CA, USA

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

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• Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, or a related quantitative field, or equivalent practical experience.
• 6 years of experience in data analysis, statistical modeling, or a similar role.
• Experience in applying machine learning techniques and economic analysis in a business context such as simulations, statistical analysis, and nonlinear regression models.
• Experience in SQL (Big Query, Postgre
  
  SQL, SQL Server, or MySQL).
• Experience in Python or R.

• Master’s degree or PhD in Data Science, Machine Learning, Statistics, Economics, or a related field.
• Experience with data visualization tools such as Tableau, Power BI, or similar.
• Experience in the consumer electronics industry, with a focus on promotional analytics and consumer offer management.
• Experience building and deploying statistical and machine learning models using Python.
• Knowledge of cloud-based AI platforms like Google Cloud’s Vertex AI for scaling machine learning models.
• Excellent communication skills, with the ability to translate complex problem solving concepts to non-technical stakeholders.

We are seeking a highly motivated Applied AI/ML Business Analytics Lead for Pixel Phones to work closely with the Head of Global Category Business Management & Consumer Offer Analytics to maximize sales and profit for the Pixel business.

As the Applied AI/ML Analytics Lead, you will work with the Head of Consumer Offer Analytics in developing models, delivering insights and informing strategic decision‑making. You will develop and implement new data‑driven processes and workflows to drive efficiency and quality in the Platforms and Devices Commercial Organization investments, and communicate data analysis in a clear and concise manner to executive leadership.

You will bring intelligence to the sales pipeline by innovating with machine learning models, simulation techniques and optimization models.

The Platforms and Devices team encompasses Google’s various computing software platforms across environments (desktop, mobile, applications), as well as our first‑party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user’s interaction with computing faster and more seamless, building innovative experiences for our users around the world.

The US base salary range for this full‑time position is $147,000‑$216,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

• Lead the development and refinement of predictive models for promotions and business. Leverage trend‑based and granular forecasting for insights to deliver strategic planning.
• Support the creation of new models to improve resource allocation and business performance across regions and partners. Integrate machine learning for automated, enhanced decision‑making.
• Provide data‑driven recommendations and scenario analysis for business. Partner with cross‑functional teams to align decisions with company goals and market opportunities.
• Examine business KPIs and discover additional opportunities to enhance our business using statistical methods to drive intelligent decision‑making.
• Communicate findings to executive leadership, ensuring clarity and insights. Enhance tools and processes, and stay informed of industry trends and technological advancements to maintain analytics practices.

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