Data Scientist

Job Description

The Data & Analytics team is responsible for integrating new data sources, creating data models, developing data dictionaries, and building machine learning models for Wholesale Bank. The primary objective is to design and deliver data products that assist squads at Wholesale Bank in achieving business outcomes and generating valuable business insights. Within this job family, we distinguish between Data Analysts and Data Scientists Requirements We are seeking a highly skilled Data Science and Machine Learning specialist with 2+ years of experience in Advanced Analytics, Statistical and ML model development. In this role, candidates will be responsible for leveraging data-driven insights and machine learning techniques to solve complex business problems, optimize processes, and drive innovation. The ideal candidate will be skilled in working with large datasets. Key Responsibilities Extract and analyse data from company databases to drive the optimization and enhancement of product development and marketing strategies. Analyse large datasets to uncover trends, patterns, and insights that can influence business decisions. Leverage predictive and AI/ML modelling techniques to enhance and optimize customer experience, boost revenue generation, improve ad targeting, and more. Design, implement, and optimize machine learning models for a wide range of applications such as predictive analytics, natural language processing, recommendation systems, and more. Conduct experiments to fine tune machine learning models and evaluate their performance using appropriate metrics. 2+ years of experience in Analytics, Machine learning, Deep learning. Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.g., Numpy, Pandas, TensorFlow, Keras, PyTorch, Scikit-learn). Strong experience with data wrangling, cleaning, and transforming raw data into structured, usable formats. Hands on experience in developing, training, and deploying machine learning models for various applications (e.g., predictive analytics, recommendation systems, anomaly detection). In depth understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning) and their appropriate use cases