Data Scientist

The Role 

The mission for the Global Data Engineering (GDE) at Bottomline is to provide central data services to all lines of businesses. Within the GDE, we are the Technology Enablement Team that is responsible for building and training ML/AI based solutions.  

Our team operates as a lean startup with full stack team members spread across multiple time zones, forward-looking and grounded in the culture of excellence and scientific approach. 

How you’ll contribute: 

• Develop and implement machine learning models and algorithms to solve complex business problems. 

• Collaborate with cross-functional teams to gather and analyse data requirements. 

• Conduct data pre-processing and cleaning to ensure data quality. 

• Create compelling data visualizations, slide decks, and stories that communicate insights, findings, and performance of developed solutions. 

• Optimise and deploy machine learning models in a production environment. 

• Monitor and maintain the performance of machine learning models. 

• Stay updated with the latest research and advancements in data science, machine learning, and artificial intelligence. 

Skills: 

• Professional degree in computer science, data science, or a related field. 

• Previous experience in data science and analysis, ML or AI. 

• Demonstrable proficiency with Python and SQL. 

• Experience with machine learning libraries and frameworks like Scikit-Learn, NumPy, pandas, plotting packages, and various packages for database access. 

• Strong understanding of statistical analysis, feature engineering and strong ability to map business problems to DS/ML solutions. 

• Ability to work with large datasets. 

• Excellent problem-solving skills and attention to detail. 

• Strong written and verbal communication skills. 

• Experience with cloud platforms such as AWS, Azure, or Google Cloud. 

• Demonstrable proficiency with Git. 

• Experience with natural language processing (NLP) and Generative AI. 

• Background in fintech or payments processing is preferable but not essential