UniOps\xe2\x80\x99 purpose unleashes the power of people, experiences, technology, data, and performance to deliver experiences that grow, fuel, and protect Unilever and our stakeholders.
The (D&A) team plays a critical role in driving Unilever\xe2\x80\x99s Growth and Fuel for Growth, empowering our people to make smarter and faster conclusions to unlock value. Our vision is to transform Unilever to become truly Data Intelligent, delivering quality performance through data-led interpretations
About D&A \xe2\x80\x93 Context and Vision
Digital technology is transforming business, creating huge amounts of data. Our belief is that those companies that can truly harness data, will be those that succeed in the future.
Our vision is to elevate data and data use across Unilever, to transform Unilever into a Data-Intelligent enterprise, where every decision and action is powered by the best data and advanced (AI) to deliver on our business goals. It\xe2\x80\x99s about empowering our employees, helping them to make smarter and faster interpretations and freeing up their capacity to focus on growth and the decisions that matter. Decisions about our categories, brands, channels, key markets, and our people.
We are helping to drive Unilever into the Future of Work, combining the best of human intelligence and machine through data-intelligent decision-making.
Who we are and what we do
We are a passionate team of over 350 data experts, data scientists, information experts, technical specialists, and business analysts. We operate as \xe2\x80\x98One D&A\xe2\x80\x99, made up of Global engagement and expertise teams, based in our key hubs including India, the UK, and US, and local UniOps in Market D&A teams, based in our key markets. Together, we combine the best of global for scale and efficiency, and local for intimacy and impact.
\xe2\x80\x98We are in the midst of a paradigm shift where the intelligence of machines will complement human ingenuity to do good, do better and smarter\xe2\x80\x9d \xe2\x80\x93 Sanjiv Mehta, Unilever President, South Asia
Our key purpose as D&A are to:
Set the Unilever data strategy and lay the foundations to maximise the value of our data\xe2\x80\x8b. Deliver the connected Data Platform to accelerate access to integrated and trusted data at scale across the company for data and decisions\xe2\x80\x8b. Embed core DA&A capabilities, leading to automation, AI, and Big Data to boost Unilever\xe2\x80\x99s data intelligence. Influence the D&A Culture to build a future-fit workforce that values and adopts data-intelligent decision-making across all levels.
We are also passionate about powering our growth with purpose, building a diverse and inclusive team and culture, for example through our #WomenInData program, and continuing to innovate in the use of data and AI to meet our sustainability goals.
Role Profile
Unilever is going through a major transformation journey. The ambition is to become a data intelligent company. To increase its data capabilities, we are looking for an experienced ML Ops manager that will help enabling our strategy through the management of reliable high performing and cost effective solutions.
We are seeking an experienced and dynamic ML Ops Manager, the Solution Factory ML Ops manager is responsible for the transition of identified Data Science solutions from development into production. They are accountable for the ongoing maintenance and performance of the Data Science production solutions.
Key Accountabilities
Work with MLOps Sr Manager to transition models into the production environment, once development and testing is complete Work with the Data Science CoE to ensure that appropriate model maintenance guidelines and monitoring framework are in place for the hand-over of model ownership from the Data Science CoE to Solution Factory DevOps Proactively monitor and manage models in production, including retraining, monitor of model quality, data, and model drifting. Plan and manage the model optimization and CI/CD automation tasks for the team Governance on the incident management, routine MLOps activities Provide regular overall quality reports to senior management Manage Data Science solution DevOps requests with the Engagement Team to deliver maintenance/ minor enhancements as appropriate Coordinate and work with Data Science CoE to deliver significant model maintenance/ enhancement work
All About You
Essential:
End to end knowledge of advanced analytics models lifecycle management Knowledge of Azure Machine Learning, Databricks, in particular experiment tracking, MLFlow and integration between MLFLow and Azure Machine Learning Services Knowledge of the most common data science algorithms and libraries (Spark MLlib, Scikit Learn\xe2\x80\xa6) Strong code lifecycle management and CI\\CD knowledge, in particular GitHub and Azure DevOps Strong operation and service management skills Wide experience on project and team management
Desirable:
Machine Learning qualification Knowledge of container technology, in particular Docker, ACS, Kubernetis Python and PySpark coding experience Deep learning tools and techniques
Leadership Skills
Demonstrates strong "Inner game":
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