We are living in dynamic times. Technology is reshaping how we live, and we want to use it to redefine how financial services are offered. GXS aims to enable the underserved groups to easily access transparent financial services that are embedded in their everyday activities, helping them achieve a better quality of life. We are incredibly excited to build a Digital Bank with the right foundation using data, technology and trust to solve problems and serve customers

Get to know the Role:

• Create and execute the data science approach in a startup atmosphere to support business goals and objectives, with commercial value of work prioritization and execution, which will deliver actionable insights for business planning and execution.
• Design, develop, and implement AI algorithms, machine learning models, and/or analytics insights for management and business teams to support various business functions, including, but not limited to: customer acquisition, customer retention, product development, pricing decisions, credit risk, fraud identification, and many other business needs within Digibank for both retail and wholesale banking customers.
• Design and develop Generative AI solutions by leveraging generative AI techniques to create new content, automate processes, and develop innovative products that offer added value to our customers, thereby enhancing productivity and operational efficiency.
• Proficiently analyze complex datasets, identify trends, and extract actionable insights to inform business decisions and strategies.
• Oversee the entire MLOps lifecycle, from model development and deployment to monitoring and maintenance, ensuring seamless integration and optimal performance.
• Actively collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to deliver innovative solutions and drive business value.

Must Haves:

• Significant relevant experience (at least 3 years of experience) in building and deploying generative AI, machine learning, and predictive model solutions on large amounts of data.
• Bachelor’s Degree in Computer Science, Applied Mathematics, Statistics, Machine Learning, or a related quantitative field.
• Extensive hands-on experience in coding and modeling skills in Spark, Python, R, SQL, Presto, Hive proficiency.
• Deep technical and data science expertise, including experience in the following:
• Analytical methods: statistical modeling (e.g., logistic regression, time series, CHAID, PCA), supervised machine learning (e.g., random forests, neural networks), unsupervised learning, design of experiments, segmentation/clustering, text mining, network analysis and graphical modeling, optimization, simulation.
• Experience building in-production models, including associated scripting, error handling, and documentation.
• Experience in cloud platforms like AWS, Azure, GCP, etc.
• Strong expertise in Generative AI frameworks and models such as GANs, VAEs, GPT, BERT, Claude, DeepSeek, etc.
• Proficient in high-quality analytics, reporting, and A/B testing.

• Create and execute the data science approach in a startup atmosphere to support business goals and objectives, with commercial value of work prioritization and execution, which will deliver actionable insights for business planning and execution.
• Design, develop, and implement AI algorithms, machine learning models, and/or analytics insights for management and business teams to support various business functions, including, but not limited to: customer acquisition, customer retention, product development, pricing decisions, credit risk, fraud identification, and many other business needs within Digibank for both retail and wholesale banking customers.
• Design and develop Generative AI solutions by leveraging generative AI techniques to create new content, automate processes, and develop innovative products that offer added value to our customers, thereby enhancing productivity and operational efficiency.
• Proficiently analyze complex datasets, identify trends, and extract actionable insights to inform business decisions and strategies.
• Oversee the entire MLOps lifecycle, from model development and deployment to monitoring and maintenance, ensuring seamless integration and optimal performance.
• Actively collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to deliver innovative solutions and drive business value