Core Job Responsibilities: - Build, Tune and Optimize Machine Learning models on Google Cloud Platform - Work with large/complex datasets to solve challeging and non-routine analysis problems, applying advanced analytical methods and techniques. - Build and prototype data pipelines for analysis at scale. - Work cross-functionally with Business Analysts and Data Engineers to help develop cutting edge and innovative artificial intelligence and machine learning models. - Make recommendations for selections on machine learning models. - Drive accuracy levels to the next stage of the given ML models. Minimum qualifications: - 2 to 5 years of relevant work experience in Machine Learning and Advanced Analytics (e.g., as a Machine Learning Specialist / Data scientist ). - Strong experience using artificial intelligence frameworks such as Tensorflow, sci-kit learn, Keras using python. - Good in Python/Nodejs programming. - Good understanding of Cloud platforms like GCP, AWS, or Azure. Must Have skills : -Work experience in building data pipelines to ingest, cleanse and transform data. - Experience of working in Manufacturing, Retail, Hi-Tech or Healthcare industry. - Applied experience with machine learning on large datasets. - Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. - Demonstrated skills in selecting the right statistical tools given a data analysis problem. - Demonstrated effective written and verbal communication skills. - Demonstrated willingness to both teach others and learn new techniques.
Job Brief and Requirements • We are looking for a Machine Learning/Natural Language Processing Engineer to help us improve our NLP products and create new NLP applications. • Experience in applying different NLP techniques to problems such as text classification, text summarization, question & answering, information retrieval, knowledge extraction, and conversational bots design potentially with both traditional & Deep Learning Techniques • NLP Skills/Tools: NLP, HMM, CRF, LDA, Word2Vec, Seq2Seq, spaCy, Nltk, Gensim, CoreNLP, NLU, NLG etc., • Ability to design & develop practical analytical approach keeping the context of data quality & availability, feasibility, scalability, turnaround time aspects. • Create language models from text data. These language models draw heavily from statistical, deep learning as well as rule based research in recent times around building taggers, parsers, knowledge graph based dictionaries etc. • Understanding of data creation. Develop highly scalable classifiers and tools leveraging machine learning and rules based models. • Work closely with product teams to implement algorithms that power user and developer-facing products. • Perform user research and evaluate user feedback.
About us DataWeave provides Retailers and Brands with “Competitive Intelligence as a Service” that enables them to take key decisions that impact their revenue. Powered by AI, we provide easily consumable and actionable competitive intelligence by aggregating and analyzing billions of publicly available data points on the Web to help businesses develop data-driven strategies and make smarter decisions. Data Science@DataWeave We the Data Science team at DataWeave (called Semantics internally) build the core machine learning backend and structured domain knowledge needed to deliver insights through our data products. Our underpinnings are: innovation, business awareness, long term thinking, and pushing the envelope. We are a fast paced labs within the org applying the latest research in Computer Vision, Natural Language Processing, and Deep Learning to hard problems in different domains. How we work? It's hard to tell what we love more, problems or solutions! Every day, we choose to address some of the hardest data problems that there are. We are in the business of making sense of messy public data on the web. At serious scale! What do we offer? - Some of the most challenging research problems in NLP and Computer Vision. Huge text and image datasets that you can play with! - Ability to see the impact of your work and the value you're adding to our customers almost immediately. - Opportunity to work on different problems and explore a wide variety of tools to figure out what really excites you. - A culture of openness. Fun work environment. A flat hierarchy. Organization wide visibility. Flexible working hours. - Learning opportunities with courses and tech conferences. Mentorship from seniors in the team. - Last but not the least, competitive salary packages and fast paced growth opportunities. Who are we looking for? The ideal candidate is a strong software developer or a researcher with experience building and shipping production grade data science applications at scale. Such a candidate has keen interest in liaising with the business and product teams to understand a business problem, and translate that into a data science problem. You are also expected to develop capabilities that open up new business productization opportunities. We are looking for someone with a Master's degree and 1+ years of experience working on problems in NLP or Computer Vision. Key problem areas - Preprocessing and feature extraction noisy and unstructured data -- both text as well as images. - Keyphrase extraction, sequence labeling, entity relationship mining from texts in different domains. - Document clustering, attribute tagging, data normalization, classification, summarization, sentiment analysis. - Image based clustering and classification, segmentation, object detection, extracting text from images, generative models, recommender systems. - Ensemble approaches for all the above problems using multiple text and image based techniques. Relevant set of skills - Have a strong grasp of concepts in computer science, probability and statistics, linear algebra, calculus, optimization, algorithms and complexity. - Background in one or more of information retrieval, data mining, statistical techniques, natural language processing, and computer vision. - Excellent coding skills on multiple programming languages with experience building production grade systems. Prior experience with Python is a bonus. - Experience building and shipping machine learning models that solve real world engineering problems. Prior experience with deep learning is a bonus. - Experience building robust clustering and classification models on unstructured data (text, images, etc). Experience working with Retail domain data is a bonus. - Ability to process noisy and unstructured data to enrich it and extract meaningful relationships. - Experience working with a variety of tools and libraries for machine learning and visualization, including numpy, matplotlib, scikit-learn, Keras, PyTorch, Tensorflow. - Use the command line like a pro. Be proficient in Git and other essential software development tools. - Working knowledge of large-scale computational models such as MapReduce and Spark is a bonus. - Be a self-starter—someone who thrives in fast paced environments with minimal ‘management’. - It's a huge bonus if you have some personal projects (including open source contributions) that you work on during your spare time. Show off some of your projects you have hosted on GitHub. Role and responsibilities - Understand the business problems we are solving. Build data science capability that align with our product strategy. - Conduct research. Do experiments. Quickly build throw away prototypes to solve problems pertaining to the Retail domain. - Build robust clustering and classification models in an iterative manner that can be used in production. - Constantly think scale, think automation. Measure everything. Optimize proactively. - Take end to end ownership of the projects you are working on. Work with minimal supervision. - Help scale our delivery, customer success, and data quality teams with constant algorithmic improvements and automation. - Take initiatives to build new capabilities. Develop business awareness. Explore productization opportunities. - Be a tech thought leader. Add passion and vibrance to the team. Push the envelope. Be a mentor to junior members of the team. - Stay on top of latest research in deep learning, NLP, Computer Vision, and other relevant areas.
The role involves image processing tasks including development, customisation and training of Neural Networks (CNNs); application of ML techniques (SVM, regression, clustering etc.) and traditional Image Processing (OpenCV etc.). The role is research focused and would involve going through and implementing existing research papers and generating new ideas. Requirements Ideal candidates should have a working knowledge of: 1) Python, Tensorflow (or similar Deep Learning frameworks), CNNs (for image classification, object detection) 2) Image Processing techniques using OpenCV or other white-box image feature extraction algos
- Strong grasp on Python and basic understanding of matrix algebra - Understanding of modern deep learning techniques like CNN, Attention, LSTM, etc - Experience with TensorFlow and Keras - Experience with Computer Vision and domain specific tools like opencv