Scientist - Machine Learning

In this role, you will have the opportunity to explore emerging technologies, creates intellectual property, and strengthens existing and future ABB offerings. We also provide R&D consultancy services to ABB Divisions and reap the benefits of open innovation by collaborating with Universities and startup companies.

You will work in dynamic and creative teams, with a wide range of experience and expertise, carrying out applied research projects developing software engineering techniques to build intelligent systems for process industries.The work model for the role is Hybrid. #LI-HybridThis role contributes to the India Corporate Research Center (INCRC), located in Bangalore.You will be mainly accountable for: Use cases and requirements gathering in cooperation with ABBs business divisions and end customers for data-driven solutions.

Evaluating and developing of mathematical algorithms and machine learning libraries for applications related to Industrial Process Automation business. Identifying and evaluating multiple design approaches and corresponding architectural solutions with a design rationale for machine learning systems.

Developing proof of concepts, prototypes, and supporting pilot evaluation and productization of solutions. Adapting and optimizing machine learning algorithms to better utilize GPU and distributed clusters. Communicating project results to experts and non-expert audiences.

Creating Intellectual Property - Invention disclosures and publishing in scientific conferences and journals. Living ABBs core values of safety and integrity, which means taking responsibility for your own actions while caring for your colleagues and the business.

• Ph.D. in a topic related to AI/ML in engineering from a reputed University with a good publication record and with a minimum of two years of relevant industrial research or post-doctoral experience.
• Proficiency in one or more programming languages and corresponding ML frameworks and libraries for interacting with industrial data.
• Good knowledge of supervised, unsupervised, semi-supervised, reinforcement learning techniques, time-series analysis, signal processing and related areas.
• Exposure to design, implementation and deployment of industrial machine learning systems with MLOps and efficient use of GPUs is preferred.
• Should have ability to collaborate and communicate effectively in English with internal and external partners including academic community.