Data Scientist/ML Engineer
Here's what you would be doing at UiPath
- Use machine learning & deep learning techniques to create new, scalable solutions for business problems.
- Develop NLP, NLU, NLG, NER, computer vision models and technologies for acquiring, parsing, interpreting and visualizing structured and unstructured data
- Running regular benchmarking tests and perform statistical analysis, draw conclusions on the impact of your research-based optimizations to provide thought leadership to the team
- Analyze and extract relevant information from large amounts data to help in automating the workflows and optimizing key processes.
- Help the team in building large scale online learning system.
- Help the team to build research to production pipeline.
- Stay current with the latest research and technology and communicate your knowledge throughout the enterprise
- Come up with patentable ideas to provide us competitive advantage.
What you will bring
- Post Graduate / Graduate in computer science or a related field and a strong math background.
- Overall 8+ years of experience in IT industry with 2-4 years working on Machine Learning & Statistics projects.
- Experience working with Machine Learning pipelines - data ingestion, feature engineering, modeling, predicting, explaining, deploying and monitoring ML models.
- Strong knowledge and 3-5 years of hands on experience with Java, Python, R, C / C++ or similar scripting languages and general software development skills (source code management, debugging, testing, deployment, etc.)
- Experience with one or more open-source toolkits such as CoreNLP, OpenNLP, NLTK, OpenCV etc.
- Experience with one or more Deep Learning frameworks like TensorFlow, PyTorch, CNTK, Caffe, Keras, DeepLearning4J etc.
- Experience with GIT, REST APIs, containerization/container management.
- Experience with Azure, GCP and/or AWS.
- PhD in Computer science or related field with focus on Deep Learning.
- Experience building Neural Networks for object detection/recognition, image classification, image segmentation, handwriting recognition.
- Experience with continuous learning & transfer learning.
- Experience with big data frameworks like Cloudera, Spark, Bigquery, & Kafka.
- Familiarity with large data sets, cloud-based development and deployment, open source practices and frameworks and experience in putting AI applications in production.
- Publications in top conferences such as NIPS, CVPR, ICLR, ICML