Your responsibilities

• Demonstrates solid competence in technologies, tools and methods in own specialist area through solving complex industrial problems.
• Maintains recognized expertise in specific area by externally reviewing current technologies.
• Contributes to the development of strategic technology plans and project portfolio in own organization.
• Performs a variety of complex technology/engineering assignments, which typically require the use of advanced and innovative techniques.
• Demonstrates sound working knowledge of relevant processes and tools and is able to train junior team members when needed.
• Ensures has solid understanding of the drivers, as well as state of- the art technologies, tools and methods in own competence area.
• Actively seeks and implements initiatives to improve operations and increase customer satisfaction.

Your background

• B.E/B.Tech in Computer Science or Electrical or Electronics from a reputed institution.
• Required 6 to 10 years of experience in usage of data Analytics and Machine learning for monitoring, diagnosis and predictive maintenance in process industry.
• Should have 3+ years of implementation experience of software or digital solutions in manufacturing sector preferably in Metals industry.
• Good knowledge of Basic Statistics (Hypothesis testing, probability, distributions, etc.).
• Should have experience in multivariate statistical Analysis (such as PCA, PLS, etc.), Machine learning (Supervised Learning techniques such as ANN, Decision Trees, SVM, Naïve Bayes etc. and Unsupervised learning techniques such as k-means, hierarchical clustering etc.), Time-series analysis, Signal Processing and related area.
• Experience with open-source Machine Learning and AI libraries (including Deep Learning, NLP etc.).
• Hands-on experience with programming languages/ tools e.g., Python, R, MATLAB, etc. preferably interacting with industrial data.
• Experience in development of proof of concepts, prototypes, and support in pilot evaluation and productization of solutions is preferred.