Introduction
As a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.

Your Role and Responsibilities
This role is primarily focused on developing ML solutions which leverage data from our products to drive insights and decision-making for our customers. The core responsibilities of the role involve supporting the automation, development and implementation of machine learning and deep learning models, management of big data infrastructure, and execution of data engineering tasks. The successful candidate will be part of our AI/ML development team who design, create, and support AI-driven products to deliver impactful AI solutions.

Your role and responsibility will encompass:
– Experience in building, training & deploying ML models and automation of these activities
– Programming in Java, Python, etc for automation of data extraction, model training, model deployment & production monitoring
– Building automated pipelines using CICD tools & technologies such as Jenkins, Travis CI, Kubernetes, Ansible, etc.
– Deploying solutions to cloud hosted systems such as IBM Cloud, AWS, Azure, GCP, etc.
– Diagnosing environmental issues and introduce/implement technologies to solve them
– Staying up-to-date on the latest container platform releases and containerization techniques
– Utilizing version control for maintaining codebase integrity and collaboration, fostering a collaborative and error-free development environment
– Managing big data infrastructure and carrying out data engineering tasks, ensuring efficient data storage, processing, and retrieval
– Staying up-to-date with the latest trends and advancements in AI/ML and related technologies, and apply this knowledge to develop innovative solutions
– Ensuring that all solutions are developed with a focus on scalability, reliability, and performance

What we offer:

• Working for a top 5 IT company according to Forbes 2022 best employers ranking
• International and prestigious projects
• Highly skilled teams of experts
• Wide range of IBM trainings and certificates
• Access to Udemy, Harvard Business Review, O’Reilly, Interskill, IBM AI Skills Academy

And what is more:

• Contract of employment
• Competitive compensation – salary range, depending on your skills and experience
• Private medical care and life insurance
• Employee Assistance Program
• Sport, charity & other networking groups
• Summer/winter camps for children
• Discounts with IBM employee badge
• Referral Bonus Program
• Home office option
• No dress code

Required Technical and Professional Expertise
• 5+ years experience in AI/ML model development and/or deployment of AI/ML workloads
• Experience designing & developing automated pipelines for data extraction, ML model training, ML model production deployments and production monitoring
• Hands-on experience from industry projects and actual validation of models for the operation, scalable deployment and operation and monitoring of machine learning applications for end users
• Background in developing and implementing automation frameworks and strategies
• Proficient in Python, with experience in coding and debugging in Python
• Hands-on experience with a cloud computing platform such as IBM Cloud, AWS, Azure, or GCP
• Experience with DevOps practices and tools such as Docker, Kubernetes, and CI/CD pipelines
• Experience with big data technologies such as Hadoop, Spark, and NoSQL databases
• Linux skills, preferably in RHEL
• Knowledgeable in applying security practices/techniques in containerized and non-containerized solutions
• Fluent in written and spoken English

Preferred Technical and Professional Expertise
• Hands-on experience with machine learning techniques and tools, including but not limited to: TensorFlow, PyTorch, scikit-learn, and XGBoost
• Familiarity in multiple programming languages (Python, Java) demonstrating versatility and adaptability is a plus
• Certification in data engineering, machine learning, or AI