Statistics Expert
Alignerr · Seattle, WA · 1 mo ago
RemoteRemoteAnalyst$25/hrContract
We need to output HTML fragment with sections: Use for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. We must not repeat job title. Must not include Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Key info:
- Engagement: Hourly, at-will contractor
- Schedule: Fully remote & asynchronous (min. 15 hrs/week)
- Pay Range (US): $25 – $100 per hour
- Start Date: Rolling — staffed as projects launch
What You’ll Do (list of duties):
- Data annotation: Accurately label and categorize statistical expressions, equations, datasets, problems, and related content
- Concept mapping: Connect statistical concepts and establish relationships between topics such as probability, distributions, hypothesis testing, regression, and statistical modeling to help AI models understand the underlying structure of statistics
- Solution verification: Analyze and provide feedback on AI-generated solutions to statistics and probability problems, identifying errors and offering step-by-step explanations to improve model accuracy
- Content development: Contribute to comprehensive training datasets covering a broad range of statistics concepts and difficulty levels
- Red teaming: Identify potential biases, inaccuracies, or limitations in the AI’s understanding of statistics and probability. Design tests to ensure AI outputs are robust and practically applicable
You’re a great fit if:
- You have deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- You are enrolled in or have completed a Bachelor’s degree or higher from an accredited institution
- You have experience building or reviewing complex data, educational, or technical content
- You possess native-level proficiency in probability and statistics, and communicate concepts clearly in written English
- Bonus: Experience with data labeling, RLHF, or other AI training projects
About The Role (section):
- Flexible workload — work from anywhere, on your own schedule
- High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
- Clear ownership — know exactly what success looks like and have autonomy to deliver
- Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs
Interview process:
- Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
- Domain-specific Zara interview to assess your understanding of fundamental statistical principles, exploring the underlying mechanisms that govern data analysis and inference.
About Labelbox:
- Labelbox builds the data engine that accelerates breakthrough AI. Our platform, expert services, and marketplace let teams iterate on data as nimbly as they iterate on code, enabling safer, smarter models in production. We’re backed by SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins, and trusted by leading research labs and enterprises worldwide.
Ready to Apply? ... (application instructions) we should remove.
We need sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule.
We have content for:
- About the role: The bullet list under "About The Role" (flexible workload, high impact, clear ownership, growth potential). Also maybe the "Engagement" etc? But those are separate.
- Responsibilities: The "What You’ll Do" list.
- Requirements: The "You’re a great fit if" list (excluding bonus maybe). Could treat as Requirements.
- Qualifications: Could be the degree requirement? Might be overlapping. But we need to decide.
- Skills: Could be deep expertise, native-level proficiency, communication, experience building/reviewing content, etc. Might be separate.
- Benefits: Not explicitly listed besides flexible workload? Could be considered benefits but we have "About the role" already includes flexible workload etc. Benefits maybe not present. The posting does not mention benefits like health insurance etc. So we should not create Benefits section unless there is real specific content. There's no explicit benefits. So omit Benefits.
- Pay: We have Pay Range (US) - $25 – $100 per hour. That's specific. So include Pay section.
- Schedule: We have Schedule - Fully remote & asynchronous (min. 15 hrs/week). That's specific. Include Schedule section.
Also maybe Engagement: Hourly, at-will contractor. Could be part of Pay? Or maybe include in About the role? But we have no section for Engagement. Could we put it under About the role? The About the role section already has bullet points; we could add "Hourly, at‑will contractor" as a bullet? But that would be adding content not originally under About the role. However we must preserve ALL substantive information; we cannot omit it. We need to include it somewhere. Could create a section "Engagement" but not in allowed headings list. The instructions say use
for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. We must not repeat job title. Must not include Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Key info:
- Engagement: Hourly, at-will contractor
- Schedule: Fully remote & asynchronous (min. 15 hrs/week)
- Pay Range (US): $25 – $100 per hour
- Start Date: Rolling — staffed as projects launch
What You’ll Do (list of duties):
- Data annotation: Accurately label and categorize statistical expressions, equations, datasets, problems, and related content
- Concept mapping: Connect statistical concepts and establish relationships between topics such as probability, distributions, hypothesis testing, regression, and statistical modeling to help AI models understand the underlying structure of statistics
- Solution verification: Analyze and provide feedback on AI-generated solutions to statistics and probability problems, identifying errors and offering step-by-step explanations to improve model accuracy
- Content development: Contribute to comprehensive training datasets covering a broad range of statistics concepts and difficulty levels
- Red teaming: Identify potential biases, inaccuracies, or limitations in the AI’s understanding of statistics and probability. Design tests to ensure AI outputs are robust and practically applicable
You’re a great fit if:
- You have deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- You are enrolled in or have completed a Bachelor’s degree or higher from an accredited institution
- You have experience building or reviewing complex data, educational, or technical content
- You possess native-level proficiency in probability and statistics, and communicate concepts clearly in written English
- Bonus: Experience with data labeling, RLHF, or other AI training projects
About The Role (section):
- Flexible workload — work from anywhere, on your own schedule
- High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
- Clear ownership — know exactly what success looks like and have autonomy to deliver
- Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs
Interview process:
- Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
- Domain-specific Zara interview to assess your understanding of fundamental statistical principles, exploring the underlying mechanisms that govern data analysis and inference.
About Labelbox:
- Labelbox builds the data engine that accelerates breakthrough AI. Our platform, expert services, and marketplace let teams iterate on data as nimbly as they iterate on code, enabling safer, smarter models in production. We’re backed by SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins, and trusted by leading research labs and enterprises worldwide.
Ready to Apply? ... (application instructions) we should remove.
We need sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule.
We have content for:
- About the role: The bullet list under "About The Role" (flexible workload, high impact, clear ownership, growth potential). Also maybe the "Engagement" etc? But those are separate.
- Responsibilities: The "What You’ll Do" list.
- Requirements: The "You’re a great fit if" list (excluding bonus maybe). Could treat as Requirements.
- Qualifications: Could be the degree requirement? Might be overlapping. But we need to decide.
- Skills: Could be deep expertise, native-level proficiency, communication, experience building/reviewing content, etc. Might be separate.
- Benefits: Not explicitly listed besides flexible workload? Could be considered benefits but we have "About the role" already includes flexible workload etc. Benefits maybe not present. The posting does not mention benefits like health insurance etc. So we should not create Benefits section unless there is real specific content. There's no explicit benefits. So omit Benefits.
- Pay: We have Pay Range (US) - $25 – $100 per hour. That's specific. So include Pay section.
- Schedule: We have Schedule - Fully remote & asynchronous (min. 15 hrs/week). That's specific. Include Schedule section.
Also maybe Engagement: Hourly, at-will contractor. Could be part of Pay? Or maybe include in About the role? But we have no section for Engagement. Could we put it under About the role? The About the role section already has bullet points; we could add "Hourly, at‑will contractor" as a bullet? But that would be adding content not originally under About the role. However we must preserve ALL substantive information; we cannot omit it. We need to include it somewhere. Could create a section "Engagement" but not in allowed headings list. The instructions say use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings? It says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Likely they expect only those headings. But we need to include engagement info somewhere. Could we incorporate it into Pay or Schedule? Pay is about rate; engagement is about contractor type. Could add a sentence in Pay section: "Hourly, at‑will contractor; Pay Range (US) – $25 – $100 per hour". That preserves info.
Similarly, Start Date: Rolling — staffed as projects launch. Could be added to About the role or maybe Schedule? Could add to About the role as a bullet? Or maybe include in About the role section as a bullet: "Start date: Rolling — staffed as projects launch". That would preserve.
We must not invent or summarize away detail. So we need to include that info exactly.
Thus:
- About the role section: include bullet points: Flexible workload — work from anywhere, on your own schedule; High impact — your craft directly improves models used by top AI labs & Fortune 500 teams; Clear ownership — know exactly what success looks like and have autonomy to deliver; Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs; Start Date — Rolling — staffed as projects launch; Engagement — Hourly, at‑will contractor.
But note that "Start Date" and "Engagement" are not originally under About the role but we are moving them there to keep headings limited. Is that allowed? The instruction: "Prefer a few substantive sections over many thin ones." It didn't forbid moving content. It said "Preserve ALL substantive information; do NOT invent or summarize away detail." Moving content is okay as long as we preserve it exactly. So we can include them under About the role.
Alternatively we could create separate sections for Start Date and Engagement but those are not in the list. Better to embed.
Now Responsibilities: list of duties.
Requirements: maybe the "You’re a great fit if" list (excluding bonus). Could treat as Requirements.
Qualifications: maybe the degree requirement? But that is already in Requirements. Could split: Requirements = experience building/reviewing content, deep expertise, native-level proficiency, communication; Qualifications = Bachelor's degree or higher. Skills = maybe experience with data labeling, RLHF, etc (bonus). But we need to avoid thin sections. Let's think.
We have:
- Deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- Enrolled in or have completed a Bachelor’s degree or higher from an accredited institution
- Experience building or reviewing complex data, educational, or technical content
- Native-level proficiency in probability and statistics, and communicate concepts clearly in written English
- Bonus: Experience with data labeling, RLHF, or other AI training projects
We could make:
Requirements: deep expertise, experience building/reviewing content, native-level proficiency & communication.
Qualifications: Bachelor's degree or higher.
Skills: Bonus experience with data labeling, RLHF, or other AI training projects (maybe also native-level proficiency? but we already used). However we need to ensure we don't create thin sections with placeholder text. Each section must have real specific content.
Thus:
- Requirements (- Deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- Experience building or reviewing complex data, educational, or technical content
- Native-level proficiency in probability and statistics, and communicate concepts clearly in written English
)
- Qualifications (
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Likely they expect only those headings. But we need to include engagement info somewhere. Could we incorporate it into Pay or Schedule? Pay is about rate; engagement is about contractor type. Could add a sentence in Pay section: "Hourly, at‑will contractor; Pay Range (US) – $25 – $100 per hour". That preserves info.
Similarly, Start Date: Rolling — staffed as projects launch. Could be added to About the role or maybe Schedule? Could add to About the role as a bullet? Or maybe include in About the role section as a bullet: "Start date: Rolling — staffed as projects launch". That would preserve.
We must not invent or summarize away detail. So we need to include that info exactly.
Thus:
- About the role section: include bullet points: Flexible workload — work from anywhere, on your own schedule; High impact — your craft directly improves models used by top AI labs & Fortune 500 teams; Clear ownership — know exactly what success looks like and have autonomy to deliver; Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs; Start Date — Rolling — staffed as projects launch; Engagement — Hourly, at‑will contractor.
But note that "Start Date" and "Engagement" are not originally under About the role but we are moving them there to keep headings limited. Is that allowed? The instruction: "Prefer a few substantive sections over many thin ones." It didn't forbid moving content. It said "Preserve ALL substantive information; do NOT invent or summarize away detail." Moving content is okay as long as we preserve it exactly. So we can include them under About the role.
Alternatively we could create separate sections for Start Date and Engagement but those are not in the list. Better to embed.
Now Responsibilities: list of duties.
Requirements: maybe the "You’re a great fit if" list (excluding bonus). Could treat as Requirements.
Qualifications: maybe the degree requirement? But that is already in Requirements. Could split: Requirements = experience building/reviewing content, deep expertise, native-level proficiency, communication; Qualifications = Bachelor's degree or higher. Skills = maybe experience with data labeling, RLHF, etc (bonus). But we need to avoid thin sections. Let's think.
We have:
- Deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- Enrolled in or have completed a Bachelor’s degree or higher from an accredited institution
- Experience building or reviewing complex data, educational, or technical content
- Native-level proficiency in probability and statistics, and communicate concepts clearly in written English
- Bonus: Experience with data labeling, RLHF, or other AI training projects
We could make:
Requirements: deep expertise, experience building/reviewing content, native-level proficiency & communication.
Qualifications: Bachelor's degree or higher.
Skills: Bonus experience with data labeling, RLHF, or other AI training projects (maybe also native-level proficiency? but we already used). However we need to ensure we don't create thin sections with placeholder text. Each section must have real specific content.
Thus:
- Requirements (- Deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- Experience building or reviewing complex data, educational, or technical content
- Native-level proficiency in probability and statistics, and communicate concepts clearly in written English
)
- Qualifications (
Enrolled in or have completed a Bachelor’s degree or higher from an accredited institution.
) (Could also be- but single item okay as
- Flexible workload — work from anywhere, on your own schedule
- High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
- Clear ownership — know exactly what success looks like and have autonomy to deliver
- Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs
- Start Date — Rolling — staffed as projects launch
- Engagement — Hourly, at‑will contractor
- Data annotation: Accurately label and categorize statistical expressions, equations, datasets, problems, and related content
- Concept mapping: Connect statistical concepts and establish relationships between topics such as probability, distributions, hypothesis testing, regression, and statistical modeling to help AI models understand the underlying structure of statistics
- Solution verification: Analyze and provide feedback on AI-generated solutions to statistics and probability problems, identifying errors and offering step-by-step explanations to improve model accuracy
- Content development: Contribute to comprehensive training datasets covering a broad range of statistics concepts and difficulty levels
- Red teaming: Identify potential biases, inaccuracies, or limitations in the AI’s understanding of statistics and probability. Design tests to ensure AI outputs are robust and practically applicable
- Deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
- Experience building or reviewing complex data, educational, or technical content
- Native-level proficiency in probability and statistics, and communicate concepts clearly in written English
.) - Skills (
Bonus: Experience with data labeling, RLHF, or other AI training projects.
) This is specific. Now Pay section: include engagement and pay range. Schedule section: include remote async and min hours. Now About the role section: include the bullets we decided. Now we need to output only HTML fragment, no markdown fences. Let's craft. Structure:About the role
Responsibilities
Requirements
Qualifications
Enrolled in or have completed a Bachelor’s degree or higher from an accredited institution.
Skills
Bonus: Experience with data labeling, RLHF, or other AI training projects.
Pay
Hourly, at‑will contractor; Pay Range (US) – $25 – $100 per hour
Schedule
Fully remote & asynchronous (minimum 15 hrs/week)
Check that we preserved all substantive info: - About the role bullets: we included flexible workload, high impact, clear ownership, growth potential, start date, engagement. Good. - Responsibilities: all five duties. - Requirements: deep expertise, experience building/reviewing content, native-level proficiency & communication. We omitted the bonus (which we put in Skills). Good. - Qualifications: Bachelor's degree or higher. - Skills: Bonus experience with data labeling, RLHF, or other AI training projects. - Pay: includes engagement and pay range. - Schedule: remote async and min hours. We omitted the interview process and About Labelbox description. Are those substantive? Yes, they are substantive. The instructions: "Pres