Data Scientists ML

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 analyze data from company databases to drive the optimization and
• enhancement of product development and marketing strategies.
• Analyze large datasets to uncover trends, patterns, and insights that can influence
• business decisions.
• Leverage predictive and AI/ML modeling 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. Qualifications:
• Bachelors, Master's or Ph.D in Computer Science, Data Science, Mathematics,
• Statistics, or a related field.
• 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 Good to Have:
• Qualification