Ingersoll Rand - Data Scientist - MLOps
Milton Roy India (p) · Bengaluru
- Experience4–8 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelmid
- Posted7 Sept 2026
About Milton Roy India (p)
Milton Roy India (p) is hiring in Bengaluru in manufacturing industrial. This role looks for around 4+ years of experience.
Skills
- MLOps
- Vertex AI
- MLflow
- Kubeflow
- CI/CD
- data quality governance
- SQL
- Python
- scikit-learn
- TensorFlow
- PyTorch
- Pandas
- GitHub Copilot
- statistical modeling
- regression
- classification
- time-series forecasting
- hypothesis testing
- IoT data architectures
- InfluxDB
- TimescaleDB
The role
A data scientist at an industrial solutions company builds production machine learning systems for IoT equipment using MLOps, statistical modeling, and time-series forecasting. The role also applies Python and SQL to govern data quality and validate analytical insights.
Full job description
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.