Job Description
Innoplexus offers Data as a Service and
Continuous Analytics as a Service products, leveraging Artificial Intelligence and advanced analytics to help reduce the time to market, significantly.
Our products leverage proprietary algorithms and patent pending technologies to help global Life sciences & Financial services organizations with access to relevant data, real time intelligence & intuitive insights, across the life cycle of the products.
We automate the collection, curation, aggregation, analysis & visualization, of billions of data points from thousands of data sources, using domain-specific language processing, ontologies, computer vision, machine learning, network analysis and more.
Key Responsibilities:
Develop solutions for realworld noisy data, large-scale problems Develop highly scalable deep learning algorithms to improve our products Develop state-of-the-art machine learning and neural network methodologies to improve our intelligence platform Use machine learning & deep learning techniques to create new, scalable solutions for business problems Develop NLP & computer vision-based tools and technologies for acquiring, parsing, interpreting and visualizing unstructured data Analyse and extract relevant information from large amounts of data to help in automating the solutions and optimizing key processes Help team in building large scale continuous/online learning systems Help team to build experimentation to production pipeline Stay up-to-date with the latest research and technology and communicate your knowledge throughout the enterprise Create IP for Innoplexus through patents and publications Required Experience:
Strong track record in AI/ML publications in renowned scientific journals or conferences Experience in any of the following: Computer Vision, Image Processing, Speech Recognition, Natural Language Understanding, Machine Learning, Deep Learning, HCI, Text Mining, Computational Genomics, Bioinformatics, other Machine Learning and Artificial Intelligence related areas Experience in Graph algorithms and Graph Embeddings(node2vec, graph2vec) Strong in Python programming Knowledge of commonly used machine learning tools: pytorch, scikit-learn, gensim, pandas Knowledge and hands-on experience in Computational Genomics and Bioinformatics is good to have Required qualification:
MS in Computer Science, Statistics, Applied Maths or related domain Degree in Computer Science, related Machine Learning field or equivalent practical
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