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Quantitative Researcher Nlp Jobs (NOW HIRING)

Vatic Labs

AI Researcher

New York, NY · On-site

$175K - $250K/yr

As an AI Researcher at Vatic Labs, you will research and develop innovative AI-driven quantitative ... NLP, vision, speech, signal processing, scientific computing, finance, etc). Finance applications ...

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Quantitative Researcher Nlp information

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$52.5K

$119.2K

$196.5K

How much do quantitative researcher nlp jobs pay per year?

As of Jun 15, 2026, the average yearly pay for quantitative researcher nlp in the United States is $119,165.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,500.00 and $152,500.00 per year, depending on experience, location, and employer.

What is the difference between Quantitative Researcher Nlp vs Quantitative Analyst?

AspectQuantitative Researcher NlpQuantitative Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of NLP and machine learningDegree in Finance, Economics, or Mathematics; strong statistical and analytical skills
Work EnvironmentTech-focused, research-driven, often in finance, tech, or research firmsFinancial institutions, investment firms, banks, focusing on data analysis and modeling
Employer & Industry UsageUsed in finance, tech, and research sectors for developing NLP models and algorithmsPrimarily in finance and investment sectors for quantitative modeling and risk assessment

While both roles involve quantitative skills, Quantitative Researcher Nlp specializes in natural language processing and machine learning techniques, often requiring programming and AI expertise. Quantitative Analysts focus more on statistical analysis, financial modeling, and data interpretation. The roles overlap in data analysis but differ in technical focus and industry application.

What are some typical challenges a Quantitative Researcher specializing in NLP might face when working on real-world data sets?

As a Quantitative Researcher in NLP, you’ll frequently encounter challenges such as handling noisy, unstructured, or imbalanced textual data, which can complicate model development and evaluation. Real-world data often lacks proper labeling, requiring creative solutions like semi-supervised learning or data augmentation. Additionally, ensuring that your models generalize well and avoid biases is critical, as is efficiently collaborating with data engineers and software developers to deploy robust solutions. Navigating these challenges effectively is key to delivering impactful NLP applications.

What are the key skills and qualifications needed to thrive as a Quantitative Researcher NLP, and why are they important?

To thrive as a Quantitative Researcher NLP, you need a solid background in mathematics, statistics, and computer science, typically supported by an advanced degree such as a master's or Ph.D. in a related field. Expertise with programming languages like Python, NLP libraries (e.g., spaCy, Hugging Face), and experience with machine learning frameworks are essential, along with familiarity with data analysis tools. Strong problem-solving skills, creativity, and effective communication help you design novel models and explain complex results to diverse audiences. These technical and interpersonal abilities are crucial for developing cutting-edge NLP solutions and collaborating effectively in research-driven teams.

What does a Quantitative Researcher in NLP do?

A Quantitative Researcher specializing in Natural Language Processing (NLP) applies mathematical, statistical, and computational techniques to analyze and model language data. Their work often involves developing algorithms to interpret, generate, or translate human language, and leveraging large datasets to uncover patterns and insights. They may work in finance, technology, or academia, using machine learning and deep learning models to solve complex language-related problems. These researchers collaborate with interdisciplinary teams to advance NLP applications, such as chatbots, sentiment analysis, and automated trading systems. Their role requires strong skills in programming, data analysis, and a deep understanding of linguistics and machine learning.
Infographic showing various Quantitative Researcher Nlp job openings in the United States as of June 2026, with employment types broken down into 11% Internship, 67% Full Time, and 22% Part Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $119,165 per year, or $57.3 per hour.
Senior Data Scientist (NLP and GenAI Specialist)

Senior Data Scientist (NLP and GenAI Specialist)

Morgan Stanley

Irving, TX • On-site

Full-time

Posted 11 days ago

Morgan Stanley rating

8.3

Company rating: 8.3 out of 10

Based on 147 frontline employees who took The Breakroom Quiz

40th of 138 rated financial services

Job description

Job Summary:
Morgan Stanley is a global leader in financial services, and they are seeking a Senior Data Scientist (NLP Specialist) to join their Non-Financial Risk team. This role involves developing and deploying advanced AI models using NLP and Machine Learning to enhance surveillance and compliance monitoring.
Responsibilities:
• Design high-performance systems leveraging cutting-edge techniques including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI architecture, and knowledge graph analytics.
• Create GenAI-based solutions to automate manual tasks and drive cost efficiency.
• Conduct research to identify novel methods for enhancing analytical solutions.
• Lead and develop junior data scientists to achieve key business objectives
• Collaborate with Compliance, Legal, Financial Crimes, and IT stakeholders to champion the adoption of new AI/ML/NLP approaches, techniques and capabilities
• Champion innovative approaches to improve detection of suspicious activity.
Qualifications:
Required:
• Master's or PhD degree in Computer Science, Machine Learning, Intelligent Systems, Statistics, Mathematics, Engineering or other highly quantitative fields
• 10+ years of hands-on industry experience in building AI/ML/NLP solutions and applied statistical analysis to solve complex business problems
• Knowledge of software design and system principles and excellent skills in either Python (preferred) or Java
• Experience with AI/ML/NLP software packages such as LangChain, LangGraph, Semantic Kernel, CrewAI, OpenAI SDK, PyTorch, HuggingFace, etc.
• Experience in adhering to Software Development Life Cycle (SDLC) principles include GIT related operations
• Strong problem solving and time management skills
• Excellent written and oral communication skills
• Familiarity with financial markets, especially in Compliance, Non-Financial Risk, and Fraud analytics
• Experience with benchmark creation and evaluation including LLM-as-a-Judge based techniques
• Knowledge of Vector Stores, Linux, SPARQL, and Graph Databases
Company:
Morgan Stanley is a financial services institution that delivers capital management, investment banking, and advisory solutions. Founded in 1935, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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