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Quantitative Researcher Nlp Jobs (NOW HIRING)

Point72

NLP Engineer

New York, NY

Research new technologies for efficient data management and data retrieval. REQUIREMENTS * PhD or ... quantitative discipline. Bachelor's degree with extensive relevant work experience will also be ...

Point72

NLP Engineer

New York, NY · On-site

Research new technologies for efficient data management and data retrieval. REQUIREMENTS * PhD or ... quantitative discipline. Bachelor's degree with extensive relevant work experience will also be ...

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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.
NLP Engineer

NLP Engineer

Point72

New York, NY

Other

Posted 27 days ago

Job description

DESCRIPTION

We are looking for a motivated and skilled NLP Engineer with experience in deep learning frameworks. This position is ideal for candidates who are eager to grow their skills in the financial industry and make a significant impact.

RESPONSIBILITIES

  • Build start-of-the-art deep learning models to process large scale unstructured datasets.
  • Engage with vendors to understand characteristics of datasets.
  • Analyze datasets to generate descriptive statistics and propose potential applications of data.
  • Conduct preliminary research and evaluation on the datasets for presentation to Portfolio Managers.
  • Research new technologies for efficient data management and data retrieval.

REQUIREMENTS

  • PhD or Master's degree in computer science, data science, statistics or other quantitative discipline. Bachelor's degree with extensive relevant work experience will also be considered.
  • At least 3 years of experience in NLP, computer vision, speech, or a related field.
  • Extensive experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
  • In-depth understanding of the architectures of modern language models, with practical experience in model implementation and training.
  • Excellent coding skills in Python.
  • Programming skills in SQL.
  • Experience working with large data sets.
  • Strong oral and written communication skills.
  • Strong team player.
  • Financial industry experience preferred but not required.
  • Candidates with top machine learning conference papers (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, NAACL) are preferred.
  • Commitment to the highest ethical standards.

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