Data Scientists ML

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: