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REQUIREMENT_ID_354 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst - SME

Company
Company jobs.micro1.ai
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary $25 - $50 per hour
Location
Location Remote
Posted Date
Posted Date Today
Statistical chart interpretation quantitative reasoning task design data visualization Python R statistical analysis written communication English proficiency remote collaboration
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Aptitude and Quantitative Reasoning Practice Open Resource β†—
Helps you sharpen the logical and numerical skills needed for the initial online assessment.
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Technical Interview Preparation Guide Open Resource β†—
Covers common technical questions and problem‑solving approaches useful for the domain interview.
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Comprehensive Interview Prep Resource Open Resource β†—
Offers interview tips, sample questions, and experience sharing for roles similar to SME positions.
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Algorithm and Data Structure Problem Set Open Resource β†—
Provides coding practice to strengthen analytical thinking and quantitative reasoning required for assessments.

Bachelor's degree or higher in Data Science, Statistics, Applied Mathematics, Computer Science, Operations Research, or a related quantitative field; minimum 60% aggregate (or CGPA 6.0/10); graduating batch 2024‑2027; no active backlogs; strong command of English (both written and spoken).

1
Round 1: Online assessment (quantitative reasoning and logical aptitude)
2
Round 2: Technical interview (domain knowledge, chart analysis, task design)
3
Round 3: HR interview (fit, communication, availability)
micro1 is an emerging AI data lab that specializes in creating high‑quality training data for frontier AI models. The company brings together experts from finance, healthcare, engineering, and many other domains to convert real‑world knowledge into structured datasets, benchmark questions, and feedback loops that help AI systems learn more effectively. By leveraging an AI‑driven recruiter, micro1 identifies and vets top talent worldwide, ensuring that every contribution meets rigorous quality standards. Their mission is to empower a billion people to do meaningful work by applying their expertise to the rapidly evolving AI ecosystem. As a Subject Matter Expert in Data & Statistical Chart Analysis, you will play a pivotal role in shaping the next generation of AI capabilities. You will work remotely as a contractor, collaborating with a global network of researchers, data engineers, and product managers. Your primary responsibility is to interpret complex statistical visualizationsβ€”such as Sankey diagrams, heatmaps, calibration curves, residual plots, correlation matrices, and distribution plotsβ€”and translate them into clear, multi‑step analytical tasks that test an AI model’s quantitative reasoning. You will also craft precise, unambiguous solutions with step‑by‑step explanations, ensuring that every answer is verifiable and grounded in proper statistical terminology. Key Responsibilities: 1. Analyze and interpret advanced data visualizations, extracting insights and identifying subtle patterns. 2. Design objective, multi‑step analytical questions that require quantitative reasoning beyond surface‑level reading. 3. Produce detailed, step‑by‑step written solutions, including calculations, statistical inferences, and modeling logic. 4. Use exact statistical language to reference axes, legends, units, scales, confidence intervals, and annotations. 5. Create tasks that involve trend analysis, comparison, interpolation, and statistical inference while avoiding ambiguity. 6. Collaborate remotely with project stakeholders through written and verbal communication, incorporating feedback promptly. 7. Ensure all deliverables are objectively verifiable and meet the quality standards required for AI training and evaluation. 8. Contribute to documentation and knowledge‑base updates for future contributors. 9. Participate in periodic review meetings to align task design with evolving project goals. 10. Maintain consistent communication and meet agreed‑upon deadlines. Tech Stack: Statistical software (R, Python‑pandas, NumPy, SciPy), data‑visualization libraries (Matplotlib, Seaborn, Plotly), version‑control (Git), and collaborative platforms (Slack, Confluence, Google Workspace). Growth Path: Starting as a contractor, high‑performing experts can transition to senior SME roles, lead specialized annotation teams, or move into advisory positions for AI model evaluation strategy. Why Join micro1? You will directly influence how cutting‑edge AI systems understand and reason about data, work with a globally distributed team of top‑tier professionals, and enjoy the flexibility of remote work while being part of a mission‑driven organization that values expertise above all else.

jobs.micro1.ai β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
jobs.micro1.ai Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for jobs.micro1.ai and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Statistical chart interpretation, quantitative reasoning, task design, data visualization, Python, R, statistical analysis, written communication, English proficiency, remote collaboration) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
jobs.micro1.ai Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join jobs.micro1.ai as a Data Analyst - SME?
Preparation Tip: Highlight jobs.micro1.ai's market reputation, recent tech innovations, and how your skills in Statistical chart interpretation directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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