About Us :

Ingersoll Rand is a global provider of mission-critical flow creation, life science and industrial solutions. Ingersoll Rand's Global Engineering & Technology Center (GEC) in Bangalore is a Great Place to Work certified workplace, driven by an ownership mindset and entrepreneurial spirit.

Job Summary :

We are looking for a technically strong Data Scientist - MLOps & Analytics Governance with 4 - 5 years of experience who will own the full MLOps lifecycle, enforce data quality governance and insights validations.

Key Responsibilities :

- Own the end-to-end MLOps lifecycle - model packaging, versioning, cloud deployment, monitoring, and automated retraining pipelines on GCP using Vertex AI, MLflow, or Kubeflow.

- Design and maintain CI/CD pipelines for ML models, ensuring reliable, repeatable deployments with full model registry traceability.

- Define and enforce data quality governance standards across all ML feature pipelines and training datasets.

- Validate model outputs and analytical findings for statistical soundness and insights validation.

- Set up model monitoring to track prediction drift, data drift, and performance degradation.

- Work with large-scale IoT sensor datasets from industrial equipment to build scalable, production-grade time-series and fault-detection pipelines.

- Collaborate with data engineers, domain experts, and product managers to translate requirements into scalable data science solutions.

- Actively use Gen AI coding assistants to accelerate development, generate boilerplate, write unit tests, and review code quality.

Mandatory Skills :

- Hands-on experience in data science, ML engineering, or applied AI roles with strong focus on production systems.

- Deep ownership of MLOps - CI/CD for ML, model versioning, deployment automation, drift monitoring, and retraining pipelines on GCP (Vertex AI) or AWS (SageMaker).

- Advanced proficiency in writing and reviewing optimised, cost-efficient SQL for large-scale workloads.

- Strong Python skills for writing and reviewing production-grade ML code using scikit-learn, TensorFlow, PyTorch, or Pandas.

- Proficient in using Gen AI coding assistants (GitHub Copilot, Claude, or similar) to boost development velocity.

- Hands-on experience implementing data quality governance and insights validation.

- Strong grounding in statistical modeling - regression, classification, time-series forecasting, and hypothesis testing.

- Familiarity with IoT data architectures - streaming pipelines, time-series databases (InfluxDB, TimescaleDB), and high-frequency sensor data processing.

What We Offer :

- Stock options (Employee Ownership Program).

- Yearly performance-based bonus.

- Comprehensive medical, life, and accident insurance.

- Employee development with LinkedIn Learning.

- Collaborative, multicultural work environment.