4P Analytics Data Strategist

hace 3 semanas

Barcelona, España buscojobs España A tiempo completo

At HP, we believe in the power of data to transform how we operate and grow. To unlock this potential, we’ve built a world-class Advanced Analytics team consisting of data scientists, data strategists, data engineers, and other skilled professionals. We work together to leverage data in ways that accelerate the company’s growth and improve decision-making across the organization.

HP ships four products a minute—whether through in-store transactions, online platforms, direct sales, or commercial channels—giving us a unique opportunity to analyze vast amounts of data. We’re committed to driving innovation in our analytics capabilities to enhance visibility and insights across our diverse customer touchpoints.

We are looking for a data-savvy, technically skilled, and business-oriented individual to join our 4P Analytics team as a Data Strategist. This role is critical to managing and industrializing the data pipeline to ensure reliable, accurate data is available for machine learning models and other advanced analytics efforts.

This role is essential in ensuring our Advanced Analytics team has the reliable, clean data they need to deliver powerful insights and drive business impact across the company. If you’re passionate about data and ready to contribute to HP’s analytics evolution, we’d love to meet you.

Responsibilities:

Data Lifecycle Management: Own the end-to-end data lifecycle, from sourcing and validation to organizing large datasets for machine learning models. You'll drive the acquisition of new data sources, establish data pipelines, and work with internal teams to ensure data is fit for purpose.

Pipeline Automation: Collaborate closely with Data Engineering to streamline and automate data pipelines. Define and document data sources and data quality standards to ensure efficient and reliable data processing that can scale as new sources are integrated.

Data Validation and Quality: Implement a rigorous data quality management approach, ensuring that the data flowing into models is clean, accurate, and consistent. Perform regular data audits and refine processes to mitigate risks related to data discrepancies or quality issues.

Data Visualization and Dashboarding: Develop intuitive visualizations and dashboards to make complex data more accessible and actionable for the team. Use tools like Power BI, Tableau, or similar platforms to create real-time, dynamic dashboards that allow teams to monitor key data points and track the effectiveness of data-driven initiatives.

Innovative Data Sourcing: Explore and identify innovative data sources (internal and external) that enhance the models' predictive accuracy. Stay on top of industry trends and emerging data sources to continuously improve the quality and breadth of available data.

Collaboration with Data Science and Engineering Teams: Work hand-in-hand with the data scientists to understand their needs and ensure the data you provide meets the technical requirements for machine learning and other advanced analytics models. Ensure smooth collaboration with the engineering team to support data pipeline scalability.

Data Documentation: Create and maintain comprehensive documentation for data pipelines, schema definitions, data transformations, and workflows to ensure transparency and ease of use for all stakeholders within the analytics team.

Optimization of Data Processes: Continuously improve data sourcing, preparation, and validation processes to optimize the efficiency of data ingestion. Automate repetitive tasks and implement tools to improve the team’s ability to manage large and complex datasets.

Job Requirements:

Master’s degree in information systems, Data Science, Computer Science, or a related field. An engineering background focused on data or analytics is ideal.

Data Expertise: Strong knowledge of data sourcing, organization, and validation techniques. Experience with large-scale datasets and working with diverse data formats (structured, unstructured, semi-structured).

TQM/Six Sigma Knowledge: Familiarity with Total Quality Management (TQM) principles or Six Sigma methodologies to build reliability into the data pipeline by default. Ability to apply these frameworks to ensure continuous improvement, minimize errors, and enhance the efficiency of data processes.

Technical Proficiency: Proficiency in programming languages like Python, R, SQL, and familiarity with tools for data transformation and pipeline development. Experience working with data pipelines, ETL processes, and cloud-based data platforms is highly desirable.

Collaboration Skills: Ability to work closely with data scientists and engineers to ensure the data pipeline supports the technical needs of the team. Experience in working within cross-functional teams in a fast-paced, dynamic environment.

Problem-Solving: Demonstrated experience in finding innovative solutions to complex data problems, whether it be new data sources, integration challenges, or optimizing data flows for speed and reliability.

Attention to Detail: Strong attention to data accuracy and quality, with an eye for potential inconsistencies or issues that could impact model outputs.

Communication Skills: Ability to document processes and communicate technical requirements and updates clearly to both technical and non-technical team members.

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