About

Loris is an AI company that develops products that help companies better handle their most difficult customer service interactions. Loris analyzes customer-agent interactions to understand customer sentiment, intent and experience to give leaders a holistic view of customer experience, identify areas of improvement for agents and their managers, and guide agents during live conversations to deliver the highest quality service with the right response at the right time.

While we’re an AI company, we believe that AI and people aren’t a zero-sum game. We design solutions to make human specialists even better at their jobs, automating the tasks that are better done through AI and machine learning methods, highlighting areas where they should focus, and recommending actions based on patterns of successful outcomes. Loris currently works with medium to large enterprises that differentiate their products and services through superior customer experience, including subscription services, eCommerce, fintech, telecom, and B2B SaaS companies.

We're based in Tel Aviv and NYC and backed by top investors, including Vertex, Floodgate, and Jeff Weiner. Working 2 days from the office while the rest from home.

Job Overview

We are looking for a motivated and creative NLP hands on researcher, to deepen our automation in conversational AI. Answering for our customers: why did the user approach customer service? Why couldn’t achieve his/her goal, what is his/er expected new action and whether he has any questions. All of these metadata needs to be extracted and generated in order to create meaningful groups and find emerging issues that our clients weren’t aware about.

Our methodology is choosing the right tools for the job and those span between Transformer based extractive Q&A, unsupervised clustering, LLM based pipelines, assortment of text classifiers, entity extraction, semantic roles classification and knowledge graph construction.

Our engineering mentality is building systems over models, hence best MLOPs and software engineering practices.

Our DS team does not write micro services and is not in charge of production code, instead our team members create software systems that encompass the whole life cycle of a data model: data querying, pre-processing, training, predicting and MLOPs (Team Lead blog about MLOPs).

If you’re passionate about data science, machine learning and NLP and you’re passionate about building impactful solutions to real-world problems, this is your opportunity.

Job Responsibilities:

● Partner end-to-end with Product Managers, Data Scientists, Software Engineers, and UX Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production

● Interleave deep learning with other methodologies (entity extraction, semantic role labeling, knowledge graph) to solve business problems at scale

● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions

● Present reports and findings to senior-level technical and non-technical audiences

● Develop processes and tools to monitor and analyze model performance and data accuracy

● Innovate, develop and test state-of-the-art approaches to solve challenging problems in conversational AI

Skills and Qualifications:

• A minimum of 4 years of experience as an Applied Data Scientist, with a strong focus on solving NLP tasks using Python.
• Strong academic background with Master's degree in Computer Science, Artificial Intelligence, or Equivalent practical experience
• Proficiency in programming languages such as Python, with the ability to write clean and efficient
• Project delivery - ability to lead a project end to end from first product meeting to defining annotations, modeling stage , engineering handover and production monitoring
• Hands-on experience in developing and deploying AI models using deep learning techniques and frameworks (e.g., TensorFlow, PyTorch, HuggingFace Transformers).
• Expertise in NLP beyond text classification in at least one of the following: Q&A (abstractive or extractive), knowledge graphs, entity extraction, semantic role labeling, token classification, topic discovery.
• Prior experience working in a startup or entrepreneurial environment is preferred