We create smart innovations to meet the mobility challenges of today and tomorrow. We design and manufacture a complete range of transportation systems, from high-speed trains to electric buses and driverless trains, as well as infrastructure, signalling and digital mobility solutions. Joining us means joining a truly global community of more than 38 900 people dedicated to solving real-world mobility challenges and achieving international projects with sustainable local impact.
Design and develop data-driven analysis, models for importing of text document, requirements, structured and non-structured data. Design models and pipeline for pre - processing of data and automatic classification based on Machine Learning /Artificial Intelligence models Identify, analyze and interpret trends and patterns in complex data to provide answers to Operational/service questions Present data and analysis in a clear and concise manner to allowing audience to quickly understand results and recommendations to make data-driven decisions Work collaboratively with engineering and product development teams and cross-functional partners develop, execute, and maintain analytic
EDUCATION B.Tech./ B.E./M.E./M.Tech./M.S. in Computer Science, Information Technology, EE, EEE,
BEHAVIORAL COMPETENCIES Be Innovative and demonstrate to peers and implement in creation of code libraries, reusable codes, and model-based developments Demonstrate excellent communication skills and able to guide, influence and convince others in a matrix organization. Team Player.
TECHNICAL COMPETENCIES & EXPERIENCE
Proven experience as developer in AI/ML/NLP Experience in data mining, Text Mining, working and creating data architectures Knowledge of Ontology is an asset Analytical mind and business acumen Strong mathematics skills (e.g. statistics, algebra, probability) Experience in cloud services e.g. PaaS and SaaS, Rest API, serverless functions Understanding of Compute Engines, VM, Containers is nice to have Problem-solving aptitude SOFTWARE SKILLS
2-5 years of proven experience in developing and implementing common NLP frameworks and languages including NLTK, Spacy, Stanford NLP, BERT, TFIDF, Wordnet Knowledge of Topic Modelling, Sentiment analysis, Summarization, Semantic analysis, Entity identification, OCR and Word Embedding Vectors and other machine learning / deep learning capabilities using unstructured text datasets.