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Remote Sentiment Analysis Jobs (NOW HIRING)

ArcSpan Media

Director/VP, Data Product

Manhattan, NY · On-site +1

$255K - $267K/yr

Experience with natural language processing, sentiment analysis. * Strong communication and ... This is a hybrid position - partially remote and partially working out of our HQ in New York City.

Krest Global Solutions

Senior Product Manager

Dallas, TX · On-site +1

$50 - $60/hr

... automation, sentiment analysis, and real-time analytics. · Develop and track key KPIs and ... US (Remote/Hybrid - Preferred locations: Brillio Offices or St. Louis, MO) Industry: Healthcare / ...

PTC

Senior Customer Experience Analyst

Boston, MA · Remote

$100K - $120K/yr

Remote About PTC PTC is transforming how the physical and digital worlds connect. Our software ... Conduct correlation and regression analysis (e.g., sentiment vs. retention, ARR impact, support ...

USAA

Data Scientist Senior

Plano, TX · On-site +1

USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with ... Embedding, Sentiment Analysis, Text Classification, and Topic Modeling * LLM Experience: Use LLM ...

PTC

Senior Customer Experience Analyst

$100K - $120K/yr

Remote About PTC PTC is transforming how the physical and digital worlds connect. Our software ... Conduct correlation and regression analysis (e.g., sentiment vs. retention, ARR impact, support ...

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All JobsRemote Sentiment Analysis Jobs

Remote Sentiment Analysis information

See salary details

$83.5K

$127K

$171K

How much do remote sentiment analysis jobs pay per year?

As of Jun 21, 2026, the average yearly pay for remote sentiment analysis in the United States is $127,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,000.00 and $143,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in remote sentiment analysis roles, and how can they be managed?

One of the main challenges in remote sentiment analysis roles is maintaining accuracy across diverse datasets, especially when interpreting nuanced language or cultural context. Working remotely can also make collaboration with team members and quick feedback loops more difficult. To overcome these issues, professionals often use collaborative platforms for regular communication, participate in ongoing training to stay updated on language trends, and rely on standardized annotation guidelines to ensure consistency. Being proactive in seeking feedback and sharing insights with the team greatly enhances both individual and project performance.

What are the key skills and qualifications needed to thrive as a Remote Sentiment Analyst, and why are they important?

To thrive as a Remote Sentiment Analyst, you need a background in linguistics, data analysis, and a strong understanding of natural language processing (NLP), often supported by a degree in a related field. Familiarity with sentiment analysis tools, machine learning platforms, and data visualization software is typically required. Strong attention to detail, critical thinking, and effective written communication help analysts interpret nuanced data and present findings clearly. These skills are essential for accurately assessing sentiment in large data sets and driving actionable insights for business or research objectives.

What is remote sentiment analysis?

Remote sentiment analysis is the process of evaluating and interpreting the emotional tone behind text data, such as social media posts, customer reviews, or emails, while working from a remote location. Professionals in this field use natural language processing (NLP) tools and machine learning algorithms to identify opinions, attitudes, or emotions expressed in written content. This information helps businesses understand customer feelings, improve products, and enhance marketing strategies. Remote sentiment analysts often collaborate with teams online and use cloud-based platforms to access and analyze large datasets. The role requires strong analytical skills, attention to detail, and proficiency with relevant software.

What is the difference between Remote Sentiment Analysis vs Remote Data Labeling Specialist?

AspectRemote Sentiment AnalysisRemote Data Labeling Specialist
Required CredentialsBasic data analysis, NLP knowledgeData annotation, labeling tools familiarity
Work EnvironmentRemote, tech companies, AI/ML projectsRemote, AI/ML, data preparation teams
Industry UsageAI, NLP, customer feedback analysisMachine learning training data creation
Common Search IntentUnderstanding sentiment analysis rolesComparing data labeling jobs

Remote Sentiment Analysis involves evaluating text data to determine sentiment, often requiring NLP skills. Remote Data Labeling Specialists focus on annotating data for machine learning models, including sentiment labels. While both roles support AI development, sentiment analysis emphasizes interpreting data, whereas data labeling involves preparing data. Candidates should consider their skills and career goals when choosing between these roles.

More about Remote Sentiment Analysis jobs
What cities are hiring for Remote Sentiment Analysis jobs? Cities with the most Remote Sentiment Analysis job openings:
What are the most commonly searched types of Sentiment Analysis jobs? The most popular types of Sentiment Analysis jobs are:
What states have the most Remote Sentiment Analysis jobs? States with the most job openings for Remote Sentiment Analysis jobs include:
What job categories do people searching Remote Sentiment Analysis jobs look for? The top searched job categories for Remote Sentiment Analysis jobs are:
Remote Sentiment Analysis jobs near you
Infographic showing various Remote Sentiment Analysis job openings in the United States as of June 2026, with employment types broken down into 5% Internship, 10% As Needed, 19% Full Time, 61% Contract, and 5% Nights. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $127,031 per year, or $61.1 per hour.

Principal Data Scientist (Remote)

Accident Fund Holdings, Inc.

Lansing, MI • On-site, Remote

Full-time

Posted 4 days ago

Job description

Job Description
SUMMARY
AF Group is seeking a Principal Data Scientist with expertise in either Commercial Property or Personal Homeowners insurance to serve as an individual contributor and technical authority on applying advanced analytics and machine learning to complex business problems, including pricing, risk selection, and other underwriting challenges. This role owns the end-to-end analytical lifecycle, from problem formulation and model development through deployment, monitoring, and governance. Partners closely with Actuarial, MLOps, and IT to deliver scalable, production-ready solutions. The Principal Data Scientist ensures long-term model performance through rigorous validation, drift monitoring, and audit-ready documentation, while advancing analytical best practices and evaluating emerging techniques relevant to commercial P&C insurance.
RESPONSIBILITIES/TASKS:
  • Acquires, organizes, and cleanses structured and unstructured data.
  • Conducts in-depth analysis to uncover trends, risks, and business opportunities.
  • Applies statistical modeling, machine learning, and advanced analytics to develop predictive and prescriptive solutions.
  • Evaluate solution performance using statistically rigorous methods and measure the impact to business outcomes.
  • Collaborate with MLOps and IT partners to transition solution prototypes from pilot validation into production environments.
  • Ensures ongoing model health through post-deployment monitoring, drift detection, and audit-compliant governance practices.
  • Creates and communicates results to senior level audiences of varying backgrounds, using business-facing presentations, reports, and dashboards.
  • Author and maintain comprehensive technical documentation for data lineage, codebases, results, and production changes.
  • Provides technical and project guidance, including peer review of work, for data science team.
  • Leads the evaluation of new analytic tools and processes.
  • Drives investigation and adoption of advanced machine learning and AI innovations.

EDUCATION:
Bachelor's Degree in Data Science, Statistics, Mathematics, Operations Research, Actuarial Science, Computer Science, Engineering, Physics or related technical field required. Advanced degree preferred.
EXPERIENCE:
10 years of experience in data science or related advanced analytics domains, including research and teaching, with 3+ years of technical leadership.
REQUIRED SKILLS/KNOWLEDGE/ABILITIES
  • 3+ years of experience supporting underwriting functions, including loss modeling, for Commercial Property (preferred) or Personal Homeowners insurance.
  • Demonstrated expertise using Poisson, Gamma, and Tweedie distributions to build loss ratio, pure premium, and frequency-severity loss models for pricing.
  • Extensive experience leveraging supervised learning models (e.g., XGBoost, GLM, etc.) and unsupervised techniques (e.g., K-means, PCA, etc.) to solve complex data science problems.
  • Advanced Python programming skills supporting data science, including scikit-learn and pandas.
  • Proficient data wrangling and ETL abilities using SQL on relational databases.
  • Comfortable explaining machine learning models with partial dependence plots and SHAP values.
  • Ability to conduct experiments e.g., A/B Testing, to evaluate the causal impact of model-driven decisions.
  • Experience using version control tools such as Git and Azure DevOps.
  • Experience working in cloud computing environments such as Azure, AWS, GCP, etc.

PREFERRED SKILLS/KNOWLEDGE/ABILITIES
  • Experience supporting at least one other commercial or personal line outside of Property lines.
  • In-depth understanding of General Liability (aka Casualty), Workers Compensation, or Commercial Vehicle insurance.
  • Knowledge of actuarial concepts and terminology used in pricing and ratemaking.
  • Experience with Claims, Marketing, or Operations functions within P&C insurance settings.
  • Ability to develop Agentic AI solutions to drive autonomous decision-making and task orchestration.
  • Familiarity with causal modeling techniques such as Meta-learners, Causal Forest, Double ML, etc.
  • Knowledge of advanced neural net architectures like LSTM, CNN, Transformers, Graph NN, etc.
  • Understanding of NLP concepts such as topic modeling, Word2Vec, sentiment analysis, OCR, etc.
  • Experience programming in the R language.
  • Ability to build interactive dashboards using frameworks such as Plotly Dash, Power BI, Flask, etc.

ADDITIONAL INFORMATION:
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. This job description does not constitute a contract for employment.
PAY RANGE:
"Actual compensation decision relies on the consideration of internal equity, candidate's skills and professional experience, geographic location, market and other potential factors. It is not standard practice for an offer to be at or near the top of the range, and therefore a reasonable estimate for this role is between $137,900 and $231,000."
We are an Equal Opportunity Employer. We will not tolerate discrimination or harassment in any form. Candidates for the position stated above are hired on an "at will" basis. Nothing herein is intended to create a contract.
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