Job Description

At Netomi AI, we are on a mission to create artificial intelligence that builds customer love for the world’s largest global brands.

Some of the largest brands are already using Netomi AI’s platform to solve mission-critical problems. This would allow you to work with top-tier clients at the senior level and build your network.

Backed by the world’s leading investors such as Y-Combinator, Index Ventures, Jeffrey Katzenberg (co-founder of DreamWorks) and Greg Brockman (co-founder & President of OpenAI/ChatGPT), you will become a part of an elite group of visionaries who are defining the future of AI for customer experience. We are building a dynamic, fast growing team that values innovation, creativity, and hard work. You will have the chance to significantly impact the company’s success while developing your skills and career in AI.

Want to become a key part of the Generative AI revolution? We should talk.

Do you believe in the missions of intelligence agencies? Are you interested in building state-of-the-art NLP models and solving complex technical challenges? Do you want to be a part of our journey in shaping the future of Automated Customer Service?

 If you are interested in working on some of the most challenging technical and programmatic issues, we would love to discuss with you the exciting work and career opportunities at Netomi. 

As a Data Scientist at Netomi, you will drive NLP and machine learning projects and be responsible for developing methodology and solutions to support technical, analytical, and operational requirements.

Job Responsibilities:

• • Design and implement machine learning algorithms for NLP/LLMs or deep learning models at scale to meet business requirements.
  • Analyze large datasets and develop models that can be used to make predictions, generate responses or summaries, identify patterns etc.
  • Develop and improve existing models to increase accuracy, efficiency, and effectiveness.
  • Work closely with cross-functional teams to identify and prioritize business problems, and develop solutions to address them.
  • Communicate findings and insights to stakeholders in a clear and concise manner.
  • Stay up-to-date with the latest developments in machine learning, NLP/LLMs, deep learning, and related technologies.

Requirements:

• • Bachelor’s or Master’s degree in Applied Mathematics, Statistics,  Computer Science or another quantitative field.
  • 2+ years of experience in Data Science or a related field, with a focus on building NLP/LLMs or Deep Learning models.
  • Strong programming skills in Python or other relevant programming languages.
  • Experience with machine learning libraries such as TensorFlow, PyTorch, or Keras.
  • Experience working with a variety of statistical models, including logistic regression, clustering, classification, SVMs, neural networks, Random Forest, CRF, Bayesian models, supervised/unsupervised learning, etc.
  • Expertise in NLP techniques, including sentiment analysis, word embedding, part-of-speech (POS) tagging, topic modeling, text classification, machine translation, speech recognition, named entity recognition (NER), natural language generation (NLG), and other related techniques.
  • Experience with various deep learning techniques, including CNNs and RNNs, and a strong understanding of building and training these models for different applications. Familiarity with LMs and LLMs such as GPT, BERT, and Transformer models is highly desirable.
  • Strong ability to rapidly comprehend and implement research papers related to AI, as well as remain informed of the latest advancements in NLP technologies.
  • Deep knowledge and experience in structured and unstructured data Information Extraction, Knowledge Information Retrieval, and Knowledge Representation.
  • Self-motivated and driven to satisfy intellectual curiosity through the pursuit of continuous learning and skill development.
  • Strong problem-solving and analytical skills
  • Excellent communication and collaboration abilities.
  • Optional: Experience with big data platforms such as Hadoop or Spark.