Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading ... Fully remote, high-trust environment that rewards curiosity, speed, and execution.
Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading ... Fully remote, high-trust environment that rewards curiosity, speed, and execution.
Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading ... Fully remote, high-trust environment that rewards curiosity, speed, and execution. Employment Type ...
Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading ... Fully remote, high-trust environment that rewards curiosity, speed, and execution. Employment Type ...
... sentiment. Your day-to-day will shift as you grow in the organization and into your role; but you ... and analysis. * Collaborate with your manager and other Associates on your team to ensure all ...
... sentiment. Your day-to-day will shift as you grow in the organization and into your role; but you ... and analysis. * Collaborate with your manager and other Associates on your team to ensure all ...
Remote Accountant (Part-Time)
Ann Arbor, MI · On-site +1
$32 - $35/hr
... sentiment. Your day-to-day will shift as you grow in the organization and into your role; but you ... and analysis. * Collaborate with your manager and other Associates on your team to ensure all ...
Remote Accountant (Part-Time)
Ann Arbor, MI · On-site +1
$32 - $35/hr
... sentiment. Your day-to-day will shift as you grow in the organization and into your role; but you ... and analysis. * Collaborate with your manager and other Associates on your team to ensure all ...
Remote Sentiment Analysis information
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?
| Aspect | Remote Sentiment Analysis | Remote Data Labeling Specialist |
|---|---|---|
| Required Credentials | Basic data analysis, NLP knowledge | Data annotation, labeling tools familiarity |
| Work Environment | Remote, tech companies, AI/ML projects | Remote, AI/ML, data preparation teams |
| Industry Usage | AI, NLP, customer feedback analysis | Machine learning training data creation |
| Common Search Intent | Understanding sentiment analysis roles | Comparing 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.
What are popular job titles related to Remote Sentiment Analysis jobs in Michigan? For Remote Sentiment Analysis jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Sentiment Analysis jobs in Michigan look for? The top searched job categories for Remote Sentiment Analysis jobs in Michigan are:
What cities in Michigan are hiring for Remote Sentiment Analysis jobs? Cities in Michigan with the most Remote Sentiment Analysis job openings:
Full-time
Posted 14 days ago
Job description
About Benzinga
Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading banks, fintechs, and AI companies worldwide.
We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering - someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests.
The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction.
Key Responsibilities
AI / Machine Learning
Backend & Infrastructure
Requirement for applying:
Required Qualification
Preferred Skills / Experience
Tech Stack
Why Join Benzinga
Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading banks, fintechs, and AI companies worldwide.
We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering - someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests.
The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction.
Key Responsibilities
AI / Machine Learning
- Research, design, and deploy machine learning models across NLP, time-series forecasting, and event detection domains.
- Build LLM-driven systems (e.g. summarization, RAG pipelines, embedding search) optimized for financial news and quantitative data.
- Develop model serving APIs and scalable inference layers using Go or Python.
- Implement model monitoring, drift detection, and continuous retraining pipelines.
- Work with financial text (earnings call transcripts, filings, news) to extract structured insights.
- Collaborate with data engineers to build training datasets, feature stores, and embedding databases.
Backend & Infrastructure
- Develop and maintain high-performance Python or Go microservices that integrate with AI systems and Go data APIs.
- Design and optimize real-time inference pipelines on AWS, leveraging ECS/EKS, S3, and Lambda.
- Ensure low-latency, fault-tolerant, and scalable delivery of AI-powered data.
- Implement CI/CD for ML workflows, including containerization, automated deployment, and versioning.
- Partner with DevOps to manage cloud infrastructure and ensure robust observability for AI workloads.
Requirement for applying:
- During your screening you will be required to submit a Loom video walkthrough of your most exceptional product, share relevant code/repo links, and describe the biggest challenge you faced building it.
Required Qualification
- 4+ years of experience in AI/ML or data engineering roles, with a proven track record of deploying ML models in production.
- Computer science degree (Bachelor minimum)
- Deep proficiency in Python (data, ML) and Go (backend, microservices).
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
- Experience with transformer architectures, embeddings, or fine-tuning LLMs.
- Strong understanding of data pipelines, feature extraction, and model lifecycle management.
- Familiarity with Docker, Kubernetes, and AWS (EKS, S3, Lambda, EC2).
- Excellent problem-solving skills and ability to work independently in a distributed environment.
Preferred Skills / Experience
- Startup experience.
- Financial services or fintech background
- Experience building LLM-powered APIs or retrieval-augmented generation (RAG) systems.
- Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch kNN).
- Experience with Kafka, LangChain, or data streaming architectures.
- Familiarity with financial data systems, real-time analytics, or news NLP.
- Exposure to MLOps tools (MLflow, BentoML, SageMaker, Airflow, etc.).
- Contributions to open-source ML or Go projects are a strong plus.
Tech Stack
- Languages: Python, Go
- ML Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain
- Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
- Containers & Orchestration: Docker, Kubernetes
- Data & Streaming: Kafka, Postgres, OpenSearch
- CI/CD: GitHub Actions, GitLab CI
- Monitoring: Datadog, Prometheus, Grafana
- Version Control: Git (Gitlab / Github)
Why Join Benzinga
- Build and ship production AI systems that shape how financial markets understand information.
- Operate with full creative freedom - explore, experiment, and execute your ideas end-to-end.
- Work with a lean, highly technical team where initiative and ownership are celebrated.
- Fully remote, high-trust environment that rewards curiosity, speed, and execution.
About Benzinga
Sourced by ZipRecruiter
Benzinga is a full-service news and media company with three main areas of expertise: real-time news, actionable trading ideas and insightful commentary. We offer coverage of all aspects of the financial market including corporate, economic and political content. With strong connections in and around the market, we strive to provide high quality and relevant news for the real-time environment that defines today's world.
Industry
Video and audio streaming services
Company size
11 - 50 Employees
Headquarters location
Detroit, MI, US
Year founded
2010