Full Stack Data Scientist

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 17 hours ago

Vacancies: 1 Vacancy

Job Summary

Full Stack Data Scientist

Req number:

R7107

Employment type:

Full time

Worksite flexibility:

Remote

Who we are

CAI is a global services firm with over 9000 associates worldwide and a yearly revenue of $1.3 billion. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients colleagues and communities. As a privately held company we have the freedom and focus to do what is rightwhatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

As the Full Stack Data Scientist you will be responsible for building deploying and managing machine learning models partnering with stakeholders to solve business problems and ensuring the operational success of productionized ML systems.

Job Description

We are looking for a Full Stack Data Scientist to lead the design development deployment and lifecycle management of machine learning models across production systems. This position will be full-time and remote.

What Youll Do

Translate business problems into ML solutions that move metrics

Partner with stakeholders and SMEs to understand the domain convert real problems into analytical form and select the right methodology (ML statistics optimization simulation)
Define success metrics evaluation approaches and validation plans (including baseline comparisons and monitoring strategy)

Build high-quality ML models (the real data science part)

Design develop and iterate on models (forecasting regression classification clustering anomaly detection etc.) with strong feature engineering and disciplined experimentation
Deliver clear decision-ready insights and communicate methods/results to technical and non-technical audiences

Engineer models into production (the ML Engineer part)

Productionize prototypes into robust ML systems with appropriate error handling versioning reproducibility and deployment patterns
Build and maintain automated pipelines for training/validation/deployment with CI/CD practices designed for ML workflows
Use AWS (SageMaker) and Databricks to operationalize training and inference workflows with a clean separation of data engineering feature engineering and model logic.

Own model lifecycle management (tracking registry governance)

Track experiments and manage model artifacts with MLflow operating a disciplined model promotion process (e.g. staging to production)
Leverage a model registry as a centralized system for model lineage/versioning and lifecycle management.

Operate production ML (monitoring alerts and continuous improvement)

Implement observability across model and data health: drift detection performance regression and actionable alerts with runbooks
Support and enhance existing production models (new features improvements reliability hardening) driving continuous improvement post-deployment.

What Youll Need

Required:

Demonstrated hands-on experience building ML models and deploying/operating them in production (end-to-end ownership)
Strong Python skills; ability to write clean testable maintainable code (refactoring modularity code review discipline)
Experience with distributed data/ML workloads in PySpark and strong SQL/data wrangling capability
Practical experience with AWS especially SageMaker and experience delivering ML workloads on Databricks
Experience with MLflow for experiment tracking and model lifecycle workflows
Strong communication skills and the ability to collaborate across functions to embed analytics into business processes

Preferred:

Experience implementing CI/CD for ML systems (tests data/contract checks packaging automated deployments)
Experience with model monitoring/drift tooling and defining retraining triggers tied to business SLAs
Experience with modern ML frameworks (e.g. PyTorch/TensorFlow) and GenAI/LLM workflows
Manufacturing/industrial analytics exposure (quality supply chain pricing forecasting).

Physical Demands

Ability to safely and successfully perform the essential job functions
Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings etc.
Ability to conduct repetitive tasks on a computer utilizing a mouse keyboard and monitor

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application interviewing completing any pre-employment testing or otherwise participating in the employment selection process please direct your inquiries to or (888).

Required Experience:

Full Stack Data ScientistReq number:R7107Employment type:Full timeWorksite flexibility:RemoteWho we areCAI is a global services firm with over 9000 associates worldwide and a yearly revenue of $1.3 billion. We have over 40 years of excellence in uniting talent and technology to power the possible fo...

Full Stack Data Scientist

Req number:

R7107

Employment type:

Full time

Worksite flexibility:

Remote

Who we are

Job Summary

Job Description

What Youll Do

Translate business problems into ML solutions that move metrics

Partner with stakeholders and SMEs to understand the domain convert real problems into analytical form and select the right methodology (ML statistics optimization simulation)
Define success metrics evaluation approaches and validation plans (including baseline comparisons and monitoring strategy)

Build high-quality ML models (the real data science part)

Design develop and iterate on models (forecasting regression classification clustering anomaly detection etc.) with strong feature engineering and disciplined experimentation
Deliver clear decision-ready insights and communicate methods/results to technical and non-technical audiences

Engineer models into production (the ML Engineer part)

Productionize prototypes into robust ML systems with appropriate error handling versioning reproducibility and deployment patterns
Build and maintain automated pipelines for training/validation/deployment with CI/CD practices designed for ML workflows
Use AWS (SageMaker) and Databricks to operationalize training and inference workflows with a clean separation of data engineering feature engineering and model logic.

Own model lifecycle management (tracking registry governance)

Track experiments and manage model artifacts with MLflow operating a disciplined model promotion process (e.g. staging to production)
Leverage a model registry as a centralized system for model lineage/versioning and lifecycle management.

Operate production ML (monitoring alerts and continuous improvement)

Implement observability across model and data health: drift detection performance regression and actionable alerts with runbooks
Support and enhance existing production models (new features improvements reliability hardening) driving continuous improvement post-deployment.

What Youll Need

Required:

Demonstrated hands-on experience building ML models and deploying/operating them in production (end-to-end ownership)
Strong Python skills; ability to write clean testable maintainable code (refactoring modularity code review discipline)
Experience with distributed data/ML workloads in PySpark and strong SQL/data wrangling capability
Practical experience with AWS especially SageMaker and experience delivering ML workloads on Databricks
Experience with MLflow for experiment tracking and model lifecycle workflows
Strong communication skills and the ability to collaborate across functions to embed analytics into business processes

Preferred:

Experience implementing CI/CD for ML systems (tests data/contract checks packaging automated deployments)
Experience with model monitoring/drift tooling and defining retraining triggers tied to business SLAs
Experience with modern ML frameworks (e.g. PyTorch/TensorFlow) and GenAI/LLM workflows
Manufacturing/industrial analytics exposure (quality supply chain pricing forecasting).

Physical Demands

Ability to safely and successfully perform the essential job functions
Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings etc.
Ability to conduct repetitive tasks on a computer utilizing a mouse keyboard and monitor

Reasonable accommodation statement

Required Experience: