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Sentiment Analysis Jobs in New York (NOW HIRING)

Syndesus, Inc.

Senior ML/AI Engineer

Manhattan, NY

$115K - $158K/yr

Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale. * Develop and iterate on the company ...

CYNET SYSTEMS

Contact Center SME

Jersey City, NJ · On-site

$75 - $80/hr

Knowledge of AI-driven CX solutions (chatbots, voice bots, Contact Lens, sentiment analysis). Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading ...

CB Insights

Senior Product Manager, Data Strategy

New York, NY · Hybrid

$138K - $182K/yr

Familiarity with NLP, text classification, sentiment analysis, or other approaches to extracting signal from unstructured content. * Background in or strong exposure to private markets, investment ...

FreshDirect

Customer Experience Analytics Intern

Bronx, NY · On-site

$23 - $25/hr

... sentiment analysis, or qualitative data coding techniques. * Experience with Power BI, Tableau, or Looker. * Experience working with customer care or CRM data (ticketing systems, support platforms)

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Sentiment Analysis information

How much do body language experts get paid?

Body language experts, often working as communication consultants or trainers, typically earn between $40,000 and $80,000 annually, depending on experience, location, and client base. Salaries can vary widely, especially for those with specialized skills or certifications in nonverbal communication and behavioral analysis.

What are the key skills and qualifications needed to thrive in the Sentiment Analysis position, and why are they important?

To excel in Sentiment Analysis, you need a solid understanding of natural language processing (NLP), data analysis, and a degree in computer science, linguistics, or a related field. Familiarity with tools such as Python (with libraries like NLTK, spaCy, or TextBlob), machine learning frameworks, and data visualization software is typically required. Strong problem-solving abilities, analytical thinking, and clear communication skills help professionals effectively interpret data and present insights. These capabilities are crucial for accurately assessing public opinion, improving customer experiences, and guiding business decisions through actionable sentiment data.

Is 40 too late for data science?

Sentiment analysis is a common data science role that values skills and experience over age. Many professionals transition into data science later in their careers by learning programming languages like Python or R and gaining relevant certifications. Age should not be a barrier if you develop the necessary technical skills and build a strong portfolio.

What is a Sentiment Analysis job?

A Sentiment Analysis job involves using natural language processing (NLP), machine learning, and data analysis techniques to evaluate and interpret emotions in text data. Professionals in this role analyze customer reviews, social media posts, and other textual content to determine sentiment as positive, negative, or neutral. They help businesses understand customer opinions, track brand perception, and improve decision-making. This role often requires skills in programming, data science, and linguistic analysis.

What is the work of sentiment analysis?

Sentiment analysis involves using natural language processing and machine learning techniques to identify and classify emotions or opinions expressed in text data. Professionals in this field develop models to analyze social media, reviews, or customer feedback, helping organizations understand public perception and make data-driven decisions.

What are some common challenges encountered in Sentiment Analysis roles?

Professionals in Sentiment Analysis often face challenges such as dealing with nuanced language, sarcasm, and context-dependent expressions that can affect analysis accuracy. Datasets may require extensive cleaning and pre-processing, and models must be continually refined to keep up with evolving language trends. Collaborating closely with product, marketing, or data science teams is vital to ensure that sentiment insights translate into actionable strategies. Staying up-to-date with advancements in NLP and regularly validating models helps maintain the quality and relevance of your analyses.

What jobs will boom in 2026?

Jobs in data analysis, artificial intelligence, and machine learning, including roles like sentiment analysis specialists, are expected to grow significantly by 2026. These roles require skills in programming, statistical analysis, and familiarity with tools like Python and natural language processing techniques.
What are the most commonly searched types of Sentiment Analysis jobs in New York? The most popular types of Sentiment Analysis jobs in New York are:
What are popular job titles related to Sentiment Analysis jobs in New York? For Sentiment Analysis jobs in New York, the most frequently searched job titles are:
What job categories do people searching Sentiment Analysis jobs in New York look for? The top searched job categories for Sentiment Analysis jobs in New York are:
Sentiment Analysis jobs near you
Infographic showing various Sentiment Analysis job openings in New York as of June 2026, with employment types broken down into 43% Full Time, 42% Part Time, 9% Temporary, and 6% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution.
Senior ML/AI Engineer

Senior ML/AI Engineer

Syndesus, Inc.

Manhattan, NY

$115K - $158K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 16 days ago

Job description

About Our Client
Our client is an applied AI and data analytics company building the intelligence layer for enterprise decision-making. Their platform unifies an organization's entire data landscape - internal systems, social media signals, industry reports, and consumer behavior data - into a single coherent intelligence layer that surfaces insights and automates workflows that historically took analysts weeks to complete.
The guiding thesis: research and data today exist in a fragmented state, with very little of it connected, and enormous value lost in that "dark data." Our client is converting sentiment from a lagging indicator into a leading one - enabling brands to make decisions months earlier than legacy research tools allow. The platform is being built toward a consumer ontology (conceptually similar to Palantir's ontology, but applied to consumer intelligence), powered by a production graph RAG system that connects signals across temporal and sentiment data at a scale that hasn't been attempted before.
The platform has driven 8-figure gross margin improvements for Fortune 500 retailers. The go-to-market motion is land-and-expand: beginning in insights and research teams, then growing into innovation, marketing, and ultimately supply chain and manufacturing.
Founded by a technical team with deep backgrounds in innovation and graph databases. The company has raised $14M in seed funding and is launching publicly after nearly two years operating in stealth. Attrition is zero - no one has left the team. Culture is a real point of pride: weekly team activities (ping pong tournaments, Yankees games, happy hours, game nights), and plus-ones are welcome at events.
This is a true ground-floor opportunity - engineers joining at this stage will have outsized influence on architecture, product direction, and culture.
About the Role
As a Senior ML/AI Engineer, you will design and ship the intelligent systems that sit at the core of the platform. This is applied AI at its most consequential - not research aimed at publishing papers, but production systems that reason, forecast, and act autonomously across complex enterprise data environments. You will build the models and agentic architectures behind demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making.
The team is running experiments at the frontier of modern technology - ML, graph databases, and agentic AI - and is looking for engineers who share the drive to stay on that edge and translate technical innovation into real product value. The role is hands-on from prototype through production, including keeping systems running reliably at scale.
Same bar as every other role on the team: senior enough to think deeply, but with the energy to roll up sleeves and execute. High agency, low ego, and a strong communicator.
Key Responsibilities
  • Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale.
  • Develop and iterate on the company's agentic AI architecture - systems that reason across heterogeneous data sources and take autonomous action.
  • Build and maintain robust ML pipelines spanning data preprocessing, feature engineering, model training, evaluation, and production deployment.
  • Architect and continuously improve the production graph RAG system, which is a core technical differentiator for the platform.
  • Design RAG systems and LLM integrations that power natural language interfaces and autonomous workflows.
  • Partner with backend engineers to ensure models are production-grade - optimized for latency, reliability, and scale.
  • Own model performance end-to-end, including monitoring, retraining, and ongoing improvement in production.
  • Stay current on AI research and bring relevant advances into the platform.
Requirements
  • 5+ years of experience in applied machine learning and AI, with models deployed and operating in production.
  • M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field - or equivalent practical experience (what you've built matters more than the degree).
  • Deep proficiency in Python, with hands-on experience across ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
  • Strong foundation in statistical analysis, predictive modeling, and time series forecasting.
  • Experience building applied agentic AI/ML systems and orchestrating multiple agents.
  • Experience with NLP, LLMs, and RAG architectures.
  • Comfort working with large-scale datasets and distributed computing environments.
Bonus Skills
  • Experience with graph databases or graph RAG systems (a major plus - core to the company's stack).
  • Background in retail, supply chain, or demand forecasting domains.
  • Experience with graph neural networks or knowledge graphs.
  • Familiarity with MLOps platforms and model serving infrastructure.
  • Open-source contributions to ML/AI projects, or published research.

Logistics
Location
New York City - 4 days/week in office. Engineering typically has Fridays flexible or remote. Additional flexibility is considered case-by-case; the team values in-person culture but treats it as a norm rather than a hard 5-day rule.
Compensation
$180,000 - $225,000 base salary, plus equity (approximately 25% of salary per year, vesting) and bonus.
Benefits & Other
Health, dental, vision, and 401(k).
Home office stipend and flexible PTO.
Ground-floor equity at a well-funded seed-stage company.
Strong team culture: weekly activities, team events, and zero attrition to date.
Interview Process
  1. Recruiter screen.
  2. Intro call with a member of the leadership team (culture and background fit).
  3. Technical screen (45-60 minutes) with a senior engineer - architecturally focused with ML depth, probing on background and hands-on ability.
  4. On-site (approximately 4 hours), covering: an ML coding interview, a system design interview focused on ML infrastructure, a product sense session (30 minutes), an AI sense session (30 minutes), and a meeting with leadership and a co-founder. Note: decisions are often made after the first two on-site interviews - most candidates do not advance through the full day.
  5. Offer.

Compensation
The base pay range for this role is $180,000 - $225,000 per year.

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