Smart Working Solutions - HRPS Machine Learning Data Engineer (Remote, Full-Time)

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[MinFit] HRPS Machine Learning Data Engineer (Remote, Full-Time)

Do you acknowledge that submitting more than one application for the same role, or providing any false or misleading information, will automatically and permanently disqualify you from the recruitment process?✱
- Yes
- No
Please respond truthfully. How many years of professional experience do you have as an ML Data Analyst / Engineer?✱
- Less than 3 years — No hands-on experience in BI/ML analytics, Python, and SQL; minimal production experience.
- 3–4 years — Solid experience in data analysis, ML analytics, and BI reporting; can independently deliver projects and dashboards.
- More than 4 years — Extensive experience leading analytics/ML projects, mentoring, and building data-driven products.
Please respond truthfully. How many years of professional experience do you have with Python (pandas, NumPy, scikit-learn)?✱
- Less than 3 years — Basic scripting and manipulation, minimal ML workflow experience.
- 3–4 years — Comfortable with data pipelines, feature engineering, and standard ML workflows.
- More than 4 years — Designs complex ML workflows, performance optimization, mentoring peers.
Please respond truthfully. How many years of professional experience do you have with SQL & Databases (PostgreSQL, DynamoDB or equivalent)?✱
- Less than 3 years — Can write simple queries, limited experience with relational/NoSQL data.
- 3–4 years — Strong query optimization, schema design, joins, aggregations, and ETL support.
- More than 4 years — Designs complex relational/NoSQL systems, large-scale ETL pipelines, and data governance.
Please respond truthfully. How many years of professional experience do you have with Power BI (Power Query / DAX / Excel advanced)?✱
- Less than 2 years — Minimal experience with dashboards, modeling, or formulas.
- 2–3 years — Builds interactive dashboards, writes DAX queries, manages data models effectively.
- More than 3 years — Designs enterprise-level dashboards, advanced Power Query transformations, mentoring peers.
Please respond truthfully. How many years of professional experience do you have with Data Warehousing & ETL?✱
- Less than 2 years — Limited exposure to pipelines or statistics (A/B testing, sampling, bias)
- 2–3 years — Comfortable designing ETL pipelines, ensuring reproducibility, and applying statistical methods.
- More than 3 years — Architecting data flows, automated pipelines, experiment tracking, bias mitigation.
Please respond truthfully. What is your level of expertise in LLM / Machine Learning Concepts & Evaluation Methods?✱
- Intermediate — Familiar with basic ML/LLM concepts, limited evaluation experience.
- Advanced — Evaluates ML models rigorously, implements bias detection, performance monitoring.
- Expert — Designs experiments, optimizes model lifecycle, mentors peers on evaluation strategies.
Please respond truthfully. What is your level of expertise in ML Ops Tooling (Feature store, Model registry, Experiment tracker, Monitoring)?✱
- Intermediate — Can use tools occasionally, limited operational impact.
- Advanced — Manages pipelines, tracks experiments, ensures reproducibility.
- Expert — Designs ML Ops infrastructure, integrates monitoring, optimizes workflows, mentors peers.
Please respond truthfully. What is your level of expertise in Data Integration & Transformation?✱
- Intermediate — Basic API/data integration skills, limited ETL experience.
- Advanced — Transforms, cleans, and structures data for dashboards and ML pipelines.
- Expert — Designs reusable, scalable pipelines across multiple sources and systems.
Please respond truthfully. What is your level of expertise in Analytical Storytelling & Client Reporting?✱
- Intermediate — Can produce dashboards, minimal storytelling.
- Advanced — Delivers insights in client-ready reports, translates complex data into actionable recommendations.
- Expert — Shapes product/strategy decisions, mentors on clear and compelling analytics communication.
Which of the following is correct for Power BI Best Practices?✱
- Avoid using proper data modeling or version control
- Use disconnected tables for metrics that should be joined
- Proper relationships, DAX measures, and reusable templates
Which of the following is correct for Python Data Pipeline Practices?✱
- Hard-code file paths or parameters
- Skip error handling in preprocessing
- Use modular, reproducible, and well-tested code
Which of the following is correct for SQL Optimization Techniques?✱
- Use SELECT * in production queries
- Ignore indexing or query performance
- Use proper joins, indexes, and query optimization
Are you comfortable working the fixed shift hours of 12 PM – 9:30 PM IST (Summer) and 1 PM – 10:30 PM IST (Winter), Monday to Friday?✱
- Yes
- No
Please respond truthfully. What is your level of spoken and written English?✱
- Fluent/Native-like or Advanced
- Intermediate or Beginner
We are looking for candidates who can start within the next 30 days. ⚠️ Providing false or misleading information will result in disqualification. Candidates progressing to the next stage will be required to upload proof of their notice period or last working day before meeting the client. When is your earliest realistic start date?✱
- I am available immediately
- I can start within 30 days
- I am currently serving my notice period and can start within 45 days
- I need more than 45 days
What is your yearly salary expectation (LPA)?✱

HRPS Machine Learning Data Engineer (Remote, Full-Time)

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[MinFit] HRPS Machine Learning Data Engineer (Remote, Full-Time)

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