Senior Manager, Artificial Intelligence (AI)/Data Science Product Management

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About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

The DnA (Data & Analytics) Sales Intelligence team at Salesforce is looking for a top-notch Product Manager to lead the development Artificial Intelligence (AI)/Machine-Learning (ML) products which help our Sales & Distribution team achieve extraordinary value. Our team is tasked with delivering intelligent data-driven strategies and products that drive Salesforce seller productivity, improve sales and operation efficiency and business excellence with actionable insights and recommendations for our sales leaders and executives.

You will use big data to empower business decisions and deliver data-driven insights, metrics, and tools in order to drive sales teams’ engagement, programs and plays design, and make data-driven actions measurable, actionable, relevant, and predictable.

The ideal candidates will have a solid background in statistics, data analytics and data science, data visualization and AI product management. They should exhibit technical skill, product sense and business savvy, with a passion for making an impact through creative storytelling and timely actions. They need to be highly analytical and creative in turning large sophisticated data into conclusions, and be able to work with several cross-functional collaborators and excel in relationship-building and collaboration.

If you are a product manager who is equally at home discussing a project with business owners, data/decision scientists, researchers or developers, grows with accelerating business growth, is willing to roll their sleeves up and do the hard work, and is very creative, collaborative, innovative – yet focused, methodical and down to earth – we want to hear from you.

Responsibilities:

• Partner closely cross functionally with sales, distribution, operations, sales strategy & program teams to turn data into insights, action, and provide recommendations to influence and shape the AI product strategy and enhance sales productivity.

• Work closely with DnA data engineers, visualization engineers, data/decisions scientists, , researchers, and other product managers to understand customer problems, find opportunities, and launch innovative AI solutions.

• Is the primary driver for identifying significant opportunities, and driving product vision, strategies and roadmaps in the context of broader organizational strategies and goals.

• Define and prioritize product requirements, user stories, and acceptance criteria based on customer and business needs, data, and feedback.

• Conduct user research, market analysis, and driven benchmarking to validate hypotheses and advise product decisions.

• Communicate and present product vision, strategy, and roadmap to various partners, including senior leadership, engineering, and sales.

• Define and analyze metrics that advise the success of products. Identify and supervise key performance metrics. Measure and evaluate product performance, user behavior, and customer satisfaction using qualitative and quantitative methods.

• Iterate and improve the product based on user feedback, data, and findings.

• Design and build intelligent recommendations engine to increase AI tool adoption and improve seller performance & business predictability.

• Partner with data architects, domain experts, data analysts and other teams to translate business requirements into KPIs and models.

• Research, prototype and build demonstrations of machine learning ideas for quick validation and feedback.

• Apply statistical models, time-series models, large language models, and causal inference methods to uncover trends, measure impact, predict future performance, and provide actionable recommendations of the enablement programs.

• Participate in architecture and code reviews. Identify, document and promote standard processes.

• Constantly learn, have a clear pulse on innovation across machine learning, data science, AI fairness.

• Be prepared for changes in business direction and understand when to adjust de