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Data Strategist Lead

4 weeks ago

Spring, United States Hewlett Packard Full time

**_Summary: _**
- We all know that a company’s ultimate goal is to increase their revenue, raise sales, and optimize costs. What better way to do this than through data? In our commercial organisation we want to invest in the power of data and that is why we have created our Advanced Analytics dream team composed of: data scientists, data strategists, data engineers, project managers, business analysts, digital analystsand a long list of highly skilled professionals that work together bringing insights from data to help the organisation thrive faster and better. Do you want to have a direct impact on our company’s growth and revenue? Don’t miss this opportunity to join our diverse and dynamic team and shape HP’s future._

HP ships 4 products a minute, resulting from transactions in store, online, direct and via our broad commercial channel. This breadth is our strength and our opportunity. At HP we are committed to driving innovation not only in our products and services but also in sales motions and analytics. Come help us enhance our visibility and insights across this amazing breadth of customer touch-points.

**_ Responsibilities: _**

Are you prepared to collaborate with internal stakeholders and channel partners to drive data acquisition and model development to support 4P MMM Analytics? Partner with leaders across disciplines such as Marketing and Category to drive significant benefits to HP.
- ** Own the relationships with key stakeholders**: Develop and nurture strong relationships with stakeholders in Category, Marketing, and Sales to deeply understand their needs and challenges.
- ** Drive optimization of investments across 4P levers**: Define the core objectives, key business questions and requirements that will shape the design and execution of data science projects. Identify needs and rework processes to actively drive optimization of investments across Promotion, Price, Placement, and Product.
- ** Guide stakeholders in investment (strategic, tactical, & operational) decisions**: Provide guidance to stakeholders in annual, semi-annual, quarterly, and intra-quarter investment optimization across key 4P levers, including Digital Demand Gen, Pricing, In-store investments, Channel Programs, and Above the Line Advertising.
- ** Manage data lifecycle and create compelling data narratives**: Full ownership of the data life cycle means creating compelling presentations, driving data acquisition, ensuring data validation, establishing new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations — always with an eye towards actionable insights.
- ** Support stakeholders with advanced analytics and scenario planning**: Utilize analytics and scenario planning to support stakeholders in decision-making processes, leveraging deeper data science models when necessary.
- ** Develop an aligned roadmap for 4P tools and analytics**: Collaborate with Category, Marketing, Sales and Data Science teams to develop an aligned roadmap for 4P tools and analytics that meets the needs of stakeholders.
- ** Drive optimization outcomes in line with objectives**: Ensure that optimization outcomes align with quarterly and annual objectives, continuously monitoring and adjusting strategies as needed.

**_ Requirements:_**
- ** Deep Business Knowledge**: Deep understanding of how various business levers work from both a consumer and commercial perspective to identify key drivers of growth and profitability within each category and channel, providing valuable insights to stakeholders.
- ** Advanced Degree in a Relevant Field**: Masters or PhD degree in Computer Science, Statistics, Mathematics, Business, Economics, Physics, Biology, or a related field.
- ** Leadership Experience**: Proven track record of success leading data science and/or data analytics teams in the marketing science domain. Ability to inspire and motivate cross-functional teams to achieve ambitious goals, fostering a culture of innovation and collaboration.
- **Technical Proficiency**: Proficiency in programming languages such as Python, R, and/or SQL.
- ** Ability to Translate Needs for Data Science Teams**: Must act as a liaison between Category and other stakeholders and the Data Science teams. Translate business needs and realities into technical requirements, guiding the development of technical roadmaps and solutions.
- ** Strong Communication and Stakeholder Management**: Excellent communication skills with the ability to effectively convey technical concepts to non-technical stakeholders. Proven ability to build and maintain strong relationships with stakeholders at all levels of the organization.

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