4 Entity Recognition Jobs

As a Data Scientist Lead-GenAI at Your Partner, a strategic technology partner specializing in AI solutions, you will be responsible for developing and deploying machine learning models and NLP solutions. With 8 to 10 years of experience, you will play a key role in optimizing performance for GenAI applications and collaborating with cross-functional teams to deliver AI-driven insights. Your expertise in Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) will be essential in driving the success of our projects. Proficiency in Python and NLP libraries such as NLTK, spaCy, TextBlob, TensorFlow, PyTorch, and Hugging Face is required. Experience with pre-trained models like BERT, GPT-3, and Transformers, as well as text preprocessing techniques including tokenization, part-of-speech tagging, lemmatization, stemming, and named entity recognition, will be highly valued. As a key member of our team, you will be expected to stay updated with the latest advancements in NLP technologies and play a crucial role in the growth and transformation of businesses. Your role will involve handling NLP tasks such as sentiment analysis, text classification, and entity recognition, while also focusing on effective stakeholder management and people leadership. Join us in changing how AI/ML is approached in the service sector and delivering outcomes that matter. Your contributions will enable businesses to grow, transform, and achieve their objectives, ultimately increasing profit and delivering improved value for customers.,

As a member of the Machine Learning team at JPMorgan Chase, you will have the opportunity to work on cutting-edge machine learning projects that optimize business decisions using the company's unique data assets. Your role will involve collecting, annotating, and enriching data for machine learning models across various lines of business within the company. You will be responsible for providing data annotation services for different data types including text, chats, emails, and audio. Understanding the business use-case will be crucial as you own the data annotation pipeline, ensuring the raw data is transformed into reliable, annotated ground truth for sophisticated machine learning methods used in banking applications such as risk assessment, trading models, customer relationship management, and pricing models. Your responsibilities will include working on data labeling tools, collaborating with stakeholders from different departments, understanding and defining relationships among entities, transcribing audio recordings, developing workflows and guidelines for data annotation, and becoming a subject matter expert in the field. To excel in this role, you should hold a Masters in Business Management (MBA) with a finance specialization, possess 5-7 years of experience in data collection, analysis, or research, and have a strong understanding of model scoring parameters and data annotation techniques. Additionally, you should be able to work both independently and as part of a team, have a keen interest in data analytics techniques, and be familiar with industry-standard annotation methods and AI paradigms. If you are someone who is detail-oriented, motivated by complex analytical problems, and eager to contribute to the advancement of machine learning applications in the financial industry, this role offers a unique opportunity to showcase your skills and expertise while collaborating with a world-class machine learning team at JPMorgan Chase.,

As a NLP scientist at Miimansa, you will play a crucial role in developing and prototyping innovative methods for various natural language understanding tasks specifically focused on clinical texts. Your responsibilities will include actively contributing to research collaborations with academic NLP research groups, participating in NLU challenge tasks, and publishing research findings. The ideal candidate should possess a Bachelor's degree or higher in computer science or engineering with a specialization in language processing. However, exceptional individuals with a Bachelor's degree in CS accompanied by relevant experience will also be considered. Additionally, you should have a minimum of 2+ years of hands-on experience in NLP and related projects, although this requirement may be flexible for candidates holding a PhD. You are expected to have a solid understanding of common NLP tasks such as text classification, entity recognition, entity extraction, and question answering. Proficiency in NLP techniques for language modeling, text representation, and semantic extraction is essential. Moreover, a good grasp of current DNN architectures, training, and evaluation methods in NLP is required, along with the ability to adapt them to clinical/medical data and issues. To excel in this role, you should possess expertise in repository management, GPU utilization, and the capability to quickly set up NLP pipelines for testing new concepts. Familiarity with NLP literature, emerging trends, prominent conference venues, and code repositories is highly valued. Proficiency in Python programming and familiarity with libraries such as Huggingface, Spacy, and OpenAI API are also crucial for this position. Furthermore, you should be willing and capable of mentoring engineering teams within the company. Miimansa is dedicated to developing technology that enhances the progression of medical interventions and patient care. Our team combines extensive expertise in clinical data-management workflows, life sciences research, and AI to address critical bottlenecks in experimental medicine, facilitating the design of more manageable and lower-risk clinical trials for sponsors. We promote hybrid working arrangements (remote/in-person) and are committed to fostering a culture of collaborative learning and exploration. Join us at Miimansa for abundant opportunities for professional growth and development.,

Responsibilities: * Design generative AI solutions using LLM, Python, SQL & NLP techniques. * Collaborate with cross-functional teams on project delivery. * Implement RNN, LSTM, GRU models for text analysis.