AVEVA is a global leader in industrial software.
Our cutting-edge solutions are used by thousands of enterprises to deliver the essentials of life – such as energy, infrastructure, chemicals, and minerals – safely, efficiently, and more sustainably.
We're the first software business in the world to have our sustainability targets validated by the SBTi, and we've been recognized for the transparency and ambition of our commitment to diversity, equity, and inclusion.
We've also recently been named as one of the world's most innovative companies.
If you're a curious and collaborative person who wants to make a big impact through technology, then we want to hear from you!
Find out more at AVEVA Careers.
The job: We are looking for a Data Scientist to play a pivotal role in solving complex business problems and discovering business insights using quantitative disciplines (statistical, algorithmic, and mining) and visualization techniques.
The role involves planning, executing, and delivering machine learning-based projects, contributing to building and developing the organization's data science models, and supporting the organization with insights and analysis for decision-making processes.
Benefits: Competitive package with an attractive bonus incentive plan, regionally specific benefits ranging from above the norm paid vacation, contributions to retirement investment plans or pensions, insurances, and many other memberships and perks designed to enhance the workplace experience, your health, and wellbeing.
Key Responsibilities: Use advanced data science techniques and approaches to solve specific business problems, exploring a variety of techniques to identify the optimal solution.
Prioritize, scope, and manage data science projects and the corresponding key performance indicators (KPIs) for success.
Identify AI/ML business opportunities and collaborate across the business to understand IT and business constraints.
Build predictive models, Machine Learning algorithms, and experiments by writing optimized code and using state-of-the-art ML technologies.
Integrate domain knowledge into the ML solution, such as understanding financial risk, customer journey, quality prediction, sales, and marketing.
Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options.
Bring new technology and approaches into production.
Adhere to and promote best practices and standards for data science processes (including documentation) and code developments.
Use data analysis, visualization, storytelling, and data technologies to scope, define, and deliver AI-based and ML data science products.
Evangelize and discover opportunities for the use of large language models (LLMs) within the organization.
Remain up to date with industry practices and emerging technologies such as generative AI and test creative ways of offering AI solutions.
Support the organization with insights and analysis for decision-making processes.
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