Minimum qualifications:

• Bachelor's degree or equivalent practical experience
• 15 years of experience in management consulting, product management and strategy, or analytics in a technology company
• Experience working with and analyzing data, and managing multiple cross-functional programs or projects
• Experience with performing market analysis and developing competitive intelligence

Preferred qualifications:

• Advanced degree or equivalent practical experience.
• Experience working with product and engineering teams
• Ability to manage executive stakeholders and communicate with a highly technical management team
• Business acumen and product intuition, with the ability to form and refine hypotheses, gather supporting data, and make recommendations
• Excellent problem solving and analysis skills, including opportunity identification, market segmentation, and framing of complex/ambiguous problems

About the job

Product and Business Strategy Leaders bring together teams across Google's functions to help products execute optimally. Our team pushes Google to scale at key points that refine our products and infrastructure by executing efficiently, bringing solid business sense and sound judgment, and working effectively across organizational lines.

Our roles often include components of strategy (e.g. analyzing and understanding new trends in the industry, building business plans), operations (e.g. running the cadence of organizations, connecting the operating lines between our functions), and communications. Our team partners with senior leadership to run important functions that cross-cut our existing organizations and deliver high impact projects. We help Engineers, PMs, UX, and all of our other functions to build amazing products that delight our users, and then get those products into their hands.

Google's Core Machine Learning team aims to build AI platforms, services, and tools behind our AI research and AI-powered products. We are owners of the Google AI frameworks (e.g., TensorFlow, JAX), AI performance and efficiency, AI training and inference platforms, AI compilers (e.g. XLA), AI software/hardware co-design (e.g. TPU), and AI developer experience (e.g., Model Hub, Eval Hub). We enable the launch of LMs (large models) from research to production and enable Google's products to adopt LMs to improve end user experiences.

As a member of the Core ML Product Operations team, you will partner with our engineering leadership, cross-functional teams (e.g., Product, Program Management, Finance, People Operations) and other partners (e.g., Research, Technical Infrastructure) to play a leading role in shaping the future for one of Google's key technical areas.

The US base salary range for this full-time position is $208,000-$296,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 .

Responsibilities

• Define frameworks for assessing the AI market landscape and customer needs, distilling these into actionable product implications.
• Leverage first-party and third-party research, amplifying relevant insights from Product Management teams and areas across the company.
• Synthesize key industry and competitive dynamics, including the development of robust industry benchmark comparisons.
• Engage in original research and analysis in areas where existing work doesn't exist, augmenting individual Product Manager analysis with a broader perspective, utilizing third-party vendors and tooling (e.g. Crayon) where appropriate.
• Create an ongoing cadence to enable distribution and actionable recommendations.