Data Analyst (Remote)
Hired · United States
- Hybrid
- Part-time
- $80,000 / year
- United States
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
Intro email — drafted to reach them directly.
Job highlights
- Analyze ML data for AI system improvement.
- Develop complex data analysis workflows.
- Collaborate with cross-functional AI teams.
- Create data visualizations and reports.
- Stay updated on AI and data analysis.
About the role
Data Analyst (Remote)
We are hiring for one of our clients, seeking a MLE Bench – Data Analyst to work on a part-time basis. This role involves hands-on analytical work with production-like datasets, metrics, and ML outputs to help evaluate, diagnose, and improve the performance of advanced AI systems. The ideal candidate is comfortable working at the intersection of data analysis and machine learning, with strong analytical rigor and the ability to work with real datasets and ML evaluation workflows.
Key Responsibilities
- Analyze structured and unstructured datasets generated from ML training, inference, and evaluation pipelines to identify trends and areas for improvement.
- Define, develop, and maintain complex data analysis workflows, including data ingestion, processing, and visualization, to support benchmark-driven evaluation projects.
- Work collaboratively with cross-functional teams, including ML engineers, researchers, and product managers, to integrate data analysis insights into AI system development and deployment.
- Develop and maintain high-quality, production-ready data visualizations and reports to communicate insights and recommendations to stakeholders.
- Stay up-to-date with the latest developments in machine learning and data analysis, and apply this knowledge to improve the accuracy and efficiency of AI system evaluations.
Required Skills & Qualifications
- Proven experience in data analysis and machine learning, with a strong understanding of statistical modeling, data visualization, and data mining techniques.
- Strong programming skills in Python, with experience working with popular data science libraries and frameworks, such as NumPy, pandas, and scikit-learn.
- Excellent analytical and problem-solving skills, with the ability to distill complex technical concepts into actionable insights and recommendations.
- Experience working with large datasets, including data ingestion, processing, and storage, using cloud-based platforms and tools, such as AWS or GCP.
- Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders, including ML engineers, researchers, and product managers.
More About the Opportunity
This role offers a unique opportunity to work with a global leader in the AI industry, contributing to the development and deployment of cutting-edge AI systems that drive business growth and innovation. As a member of the MLE Bench team, you will have the opportunity to work with a talented and diverse team of researchers, engineers, and product managers, and to apply your skills and expertise to real-world problems and challenges.
Equal Opportunity Employer
We hire based on skills and expertise. All qualified candidates are welcome regardless of background, experience, or prior employment history. Applications are reviewed solely on demonstrated technical ability and qualifications.
Key skills/competency
- Data Analysis
- Machine Learning
- Python
- Statistical Modeling
- Data Visualization
- Data Mining
- AWS
- GCP
- Problem-Solving
- Communication
Skills & topics
- Data Analyst
- Data Analysis
- Machine Learning
- Python
- Statistical Modeling
- Data Visualization
- Data Mining
- AWS
- GCP
- AI
- Part-time
- Remote
How to get hired
- Tailor your resume: Highlight Python, data analysis, and ML experience.
- Showcase ML projects: Quantify achievements with data and metrics.
- Prepare for technical questions: Review Python libraries and ML concepts.
- Demonstrate collaboration: Emphasize cross-functional teamwork experience.
- Apply with confidence: Follow instructions precisely for your application.
Technical preparation
Master Python data science libraries.,Practice SQL for data extraction.,Review statistical modeling concepts.,Build ML evaluation workflows.
Behavioral questions
Describe a complex data problem solved.,How do you collaborate with technical teams?,How do you present data insights?,How do you stay updated on AI trends?
Frequently asked questions
- What specific data analysis tasks are involved for a Data Analyst at Hired?
- As a Data Analyst at Hired, you will analyze datasets from ML training and evaluation pipelines, define data workflows, and create visualizations to improve AI system performance.
- What programming languages and tools are essential for this Data Analyst role?
- Strong programming skills in Python, including libraries like NumPy, pandas, and scikit-learn, are essential. Experience with cloud platforms like AWS or GCP is also required.
- How does this Data Analyst position contribute to AI system development?
- This role directly contributes by evaluating and improving AI systems through rigorous data analysis, integrating insights into development, and ensuring efficient ML model performance.
- What kind of collaboration is expected for the Data Analyst role?
- You will collaborate with ML engineers, researchers, and product managers, integrating data analysis insights to enhance AI system development and deployment.
- Is this Data Analyst role full-time or part-time?
- This Data Analyst role is specifically mentioned as being on a part-time basis.
- What is the company culture like for a Data Analyst at Hired?
- Hired emphasizes hiring based on skills and expertise, welcoming all qualified candidates regardless of background. Applications are reviewed based on demonstrated technical ability and qualifications.
- What are the key qualifications for the Data Analyst position?
- Key qualifications include proven experience in data analysis and machine learning, strong Python programming skills, excellent analytical abilities, and experience with large datasets and cloud platforms.