Quantitative Analysis, Research
Global Wealth Management
Job Reference #
Are you passionate about directly impacting the business by utilizing analytical and machine learning skills? Do you want to play a key role in mining large datasets, build advanced statistical models to generate meaningful and actionable insights, improve decision making, optimize the business process, and help address business problems?
We're are looking for Data Scientist to:
• work with stakeholders to identify opportunities to leverage data to address business problems and drive business results
• mine and analyze data from various data sets to provide actionable insights
• build and deploy machine learning and statistical methods to optimize the business process and help with targeted marketing and sales efforts
• develop processes and tools to monitor and analyze model performance and data accuracy
• produce relevant documentation related to model performance, model review and model governance
You will be part of the Data Science team in Smart Technologies and Advanced Analytics Team (STAAT), WMA in our Krakow location, Fabryczna Office Park. Our Data Science team is at the heart of STAAT’s function to manage and support data science efforts across different business areas. Our team is global based in US, Poland, China, and Switzerland. We are open to Hybrid/remote work schedule. At the heart of this project is the ability to systematically analyze and build statistical and machine learning models on companies and markets that we do research on. Our main goal is for business to make better decisions.
Diversity helps us grow, together. That’s why we are committed to fostering and advancing diversity, equity, and inclusion. It strengthens our business and brings value to our clients.
• master's degree or PhD in Data Science, Machine Learning, Statistics, or related STEM fields
• 5+ years of relevant or equivalent experience, experience in finance industry is a plus
• knowledge of a variety of machine learning techniques such as classification, clustering, optimization, Random Forest, PCA, XgBoost, natural language processing, deep neural network, etc
• good understanding of mathematical underpinning and their real-world advantages/drawbacks
• hands on experience of using programming languages (Python, R, SQL, etc.) to manipulate data, develop models and derive insights
• hands on experience of database and analytical technologies in the industry, such as Greenplum, DB2, Dataiku, Hadoop, etc
• hands-on experience deploying analytical models to solve business problems
• ability to develop experimental and analytical plans for data modeling processes and A/B testing
• experience with Tableau will be an asset
UBS is the world’s largest and only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.
With more than 70,000 employees, we have a presence in all major financial centers in more than 50 countries. Do you want to be one of us?
How we hire
This role requires an assessment on application. Learn more about how we hire: www.ubs.com/global/en/careers/experienced-professionals.html
At UBS, we embrace flexible ways of working when the role permits. We offer different working arrangements like part-time, job-sharing and hybrid (office and home) working. Our purpose-led culture and global infrastructure help us connect, collaborate, and work together in agile ways to meet all our business needs.
From gaining new experiences in different roles to acquiring fresh knowledge and skills, we know that great work is never done alone. We know that it's our people, with their unique backgrounds, skills, experience levels and interests, who drive our ongoing success. Together we’re more than ourselves. Ready to be part of #teamUBS and make an impact?
Disclaimer / Policy Statements
UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.