Data Scientist

Introduction
In this role you’ll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers) where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world.​ Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.

Your Role and Responsibilities

As a Data Scientist at IBM you will work to solve business problems using leading edge and open-source tools such as Python R and TensorFlow combined with IBM tools and our AI application suites. You will prepare analyze and understand data to deliver insight predict emerging trends and provide recommendations to stakeholders. In your role you may be responsible for:

• Implementing and validating predictive and prescriptive models and creating and maintaining statistical models with a focus on big data & incorporating machine learning. techniques in your projects
• Writing programs to cleanse and integrate data in an efficient and reusable manner
• Working in an Agile collaborative environment partnering with other scientists engineers consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviors
• Communicating with internal and external clients to understand and define business needs and appropriate modelling techniques to provide analytical solutions.
• Evaluating modelling results and communicating the results to technical and non-technical audiences.

Required Technical and Professional Expertise
• Education: Master’s degree in AI Machine Learning or a related field; or PhD can be in AI Mathematics Physics or a closely related discipline
• 1+ years of experience in machine learning and AI model development (tunning training)
• Programming skills in Python (with machine learning frameworks and libraries (e.g. TensorFlow PyTorch scikit-learn cuda ONNX torce serve).
• Understanding of deep learning architectures: Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) networks Generative Adversarial Networks (GANs) and Transformers.
• Knowledge of data preprocessing augmentation and pipeline development strong on linguistic annotation and NLP

Preferred Technical and Professional Expertise
• Very good in multi-threading – this is a must and clean code
• Version control systems Git and collaborative development tools (Jira)
• Strong background in data science research including experience with statistical analysis data mining data visualization techniques and leveraging large datasets to drive insights and model improvements.
• Excellent problem solving skills and ability to work in a fast-paced collaborative environment.
• Strong communication skills and ability to document technical processes effectively.