Innovation and Data Science Lead

Roles & Responsibility

Reinforce data governance and quality of HR master data.

Data governance and ethics is considered for all PA solutions and global dashboards.
Challenge market solutions and teams where data governance is not being enforced.
Support Data quality program.

Stakeholder Management

Support global, zone, and market teams by listening to their needs and challenges. Engage in proactive discussions with key stakeholders. Build rapport and trust. Create channels for ongoing listening and feedback.
Create valuable integration with the Zone PA, CoC, and HR teams. Advise on all areas of Innovation and Data Science. Integrate key innovation and data science solutions into the zone projects/priorities.

Experiment and create scalable global data science solutions

Enable a data structure for innovation and solution development
Identify new data points which can be leveraged across global solutions and data science projects. Share with the I&DS team for alignment and feedback.
Work with IT HR and ADI to access new data points and make these available within our new data science landscape.
Support with defining the method and location for storage of new data points – work alongside IT to understand what is possible, how this will impact data science solutions, and share achievements / learnings with the team.

Build and scale new solutions to maximise the value of Nestle data:

Develop E2E from business understanding to data prep, through modelling and delivery, at least 2 new data science models to be used by People Analytics teams globally with the potential to scale for self-service within markets.
Collaborate with ZNA to design, develop and deploy global solutions.
Find opportunities to improve, extend and automate global PA dashboards using data science techniques and newly created data.
Support the adoption and engagement of industrialised data science solutions across all Zones and Markets.
Role model best practice in solution design through application in data science solution build.

Drive Innovation in Data Science with experimentation

Explore and share new techniques and technologies which can evolve our data science offerings to the next level.
Experiment and test new techniques and technologies to evaluate suitable for solution design and build.

Data Science architecture and Ways of working.

Align and support ways of working within the team to support innovation and global solutions
Align to and implement agile working into daily and weekly task delivery, including sprints and DevOps practices.
Actively contribute to the teams ways of working, process designs, comms and change management approach.
Support and coach colleagues within the I&DS team to succeed with advice on designing, building, and deploying solutions using data science and ML models.
As part of the team, understand market needs and identify product/solution gaps. Contribute towards evaluation and prioritisation of ideas into agile ways of working and product roadmap.

Design and setup the architecture required to successfully build and deploy data science solutions

Lead on the design of the new data science architecture through collaboration with IT HR and ADI. Identify needs for own projects and collaborate across I&DS team to align on shared needs.
Collaborate and integrate with IT HR and ADI to implement new data science architecture and tools to support own project’s needs, and support the needs of the wider team.
Leverage expertise outside of immediate team to close any knowledge gaps, and support the needs of the team in the data science space.
Effectively highlight, document, and communicate gaps in architecture. Ensure any identified risks and concerns are capture and addressed appropriately.
Explore and share external best practice on data science architecture.

Tools Used: Azure ML Studio, ADF, Snowflake, python, SQL, Sckit-Learn, matplotlib

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Tools Used: Azure ML Studio, ADF, Snowflake, python, SQL, Tensorflow, pytorch-geometric