Data Scientist India

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Mercor is hiring a Data Scientist to help build advanced analytics and data-driven infrastructure for its AI lab partner focused on developing intelligent agent-based systems. This role is ideal for analytical thinkers who excel at turning large-scale data into actionable insights and enjoy working at the intersection of machine learning experimentation and real-world applications. Youll be designing data pipelines statistical models and performance metrics that drive the next generation of autonomous systems.

Youre a great fit if you:

Have a strong background in data science machine learning or applied statistics.
Are proficient in Python SQL and familiar with libraries such as Pandas NumPy Scikit-learn and PyTorch/TensorFlow.
Understand probabilistic modeling statistical inference and experimentation frameworks (A/B testing causal inference).
Can collect clean and transform complex datasets into structured formats ready for modeling and analysis.
Have experience designing and evaluating predictive models using metrics like precision recall F1-score and ROC-AUC.
Are comfortable working with large-scale data systems (Snowflake BigQuery or similar).
Are curious about AI agents and how data can shape the reasoning adaptability and behavior of intelligent systems.
Enjoy collaborating with cross-functional teams from engineers to research scientists to define meaningful KPIs and experiment setups.

Primary Goal of This Role

To design and implement robust data models pipelines and metrics that support experimentation benchmarking and continuous learning for agentic AI systems. The role focuses on building data-driven insights into how agents reason perform and improve over time across algorithmic and real-world tasks.

What Youll Do

Develop data collection and preprocessing pipelines for structured and unstructured data from multiple agent simulations.
Build and iterate on machine learning models for performance prediction behavior clustering and outcome optimization.
Design and maintain dashboards and visualization tools for monitoring agent performance benchmarks and trends.
Conduct statistical analyses to evaluate the efficacy of AI systems under various environments and constraints.
Collaborate with engineers to design evaluation frameworks that measure reasoning quality adaptability and efficiency.
Prototype data-driven tools and feedback loops to automatically improve model accuracy and agent behavior over time.
Work closely with AI research teams to translate experimental results into scalable production-grade insights.

Why This Role Is Exciting

Work at the forefront of AI agent intelligence and help define how data shapes their evolution.
Blend machine learning experimentation and data engineering in one role.
Collaborate with top-tier AI engineers on new agent benchmarks and feedback mechanisms.
Contribute to a mission that merges algorithmic reasoning real-world performance and human-like decision-making.

Pay & Work Structure

Youll be classified as an hourly contractor to Mercor.
Paid weekly via Stripe Connect based on hours logged.
Part-time (20 hrs - 40 hrs/week) with fully remote async flexibility work from anywhere on your own schedule.
Weekly bonus of $500 - $1000 USD per 5 task created.

Mercor is hiring a Data Scientist to help build advanced analytics and data-driven infrastructure for its AI lab partner focused on developing intelligent agent-based systems. This role is ideal for analytical thinkers who excel at turning large-scale data into actionable insights and enjoy working ...

Youre a great fit if you:

Have a strong background in data science machine learning or applied statistics.
Are proficient in Python SQL and familiar with libraries such as Pandas NumPy Scikit-learn and PyTorch/TensorFlow.
Understand probabilistic modeling statistical inference and experimentation frameworks (A/B testing causal inference).
Can collect clean and transform complex datasets into structured formats ready for modeling and analysis.
Have experience designing and evaluating predictive models using metrics like precision recall F1-score and ROC-AUC.
Are comfortable working with large-scale data systems (Snowflake BigQuery or similar).
Are curious about AI agents and how data can shape the reasoning adaptability and behavior of intelligent systems.
Enjoy collaborating with cross-functional teams from engineers to research scientists to define meaningful KPIs and experiment setups.

Primary Goal of This Role

What Youll Do

Develop data collection and preprocessing pipelines for structured and unstructured data from multiple agent simulations.
Build and iterate on machine learning models for performance prediction behavior clustering and outcome optimization.
Design and maintain dashboards and visualization tools for monitoring agent performance benchmarks and trends.
Conduct statistical analyses to evaluate the efficacy of AI systems under various environments and constraints.
Collaborate with engineers to design evaluation frameworks that measure reasoning quality adaptability and efficiency.
Prototype data-driven tools and feedback loops to automatically improve model accuracy and agent behavior over time.
Work closely with AI research teams to translate experimental results into scalable production-grade insights.

Why This Role Is Exciting

Work at the forefront of AI agent intelligence and help define how data shapes their evolution.
Blend machine learning experimentation and data engineering in one role.
Collaborate with top-tier AI engineers on new agent benchmarks and feedback mechanisms.
Contribute to a mission that merges algorithmic reasoning real-world performance and human-like decision-making.

Pay & Work Structure

Youll be classified as an hourly contractor to Mercor.
Paid weekly via Stripe Connect based on hours logged.
Part-time (20 hrs - 40 hrs/week) with fully remote async flexibility work from anywhere on your own schedule.
Weekly bonus of $500 - $1000 USD per 5 task created.