Data Scientist

Ingersoll-Rand plc; Bengaluru; On-site; INR 3,000,000 - 4,800,000; Full time

Benefits offered by this job

Stock options

Yearly bonus

Leave encashments

Maternity/Paternity leaves

Health insurance

Employee Assistance Program

Learning opportunities

Awardco recognition

Collaborative environment

Job summary

Ingersoll Rand in Bengaluru seeks a Data Scientist – MLOps & Analytics Governance to own the full ML lifecycle, enforce data quality, and validate insights across cloud deployments.

You will write scalable Python and SQL, work with BigQuery on GCP, and collaborate with engineers and product teams to deliver reliable models and measurable business impact.

Experience with Generative AI assistants and IoT data is a plus, with emphasis on production-grade pipelines and governance.

Qualifications

• Hands-on data science or ML engineering with production systems experience.
• Strong MLOps ownership: CI/CD for ML and model deployment.
• Advanced SQL skills for large-scale workloads.
• Production-grade Python for feature engineering and inference pipelines.
• Data quality governance and automated profiling experience.
• Insights validation to prevent data leakage and biased evaluations.
• Statistical modeling and time-series forecasting fundamentals.
• Familiarity with IoT data architectures and streaming data.

Responsibilities

• Own end-to-end MLOps lifecycle including packaging, deployment, monitoring and retraining on GCP.
• Design CI/CD pipelines for ML with full model registry traceability.
• Define and enforce data quality governance across ML pipelines.
• Validate model outputs for statistical soundness before stakeholder delivery.
• Set up model monitoring for drift and performance degradation.
• Work with IoT sensor data to build scalable time-series pipelines.
• Collaborate with data engineers and product managers; leverage Gen AI helpers to accelerate development.

Skills

Python

MLOps

SQL

Vertex AI

CI/CD for ML

Data quality

Time-series

BigQuery

Gen AI assistants

Education

B.Tech / M.Tech (CS/DS)

Cloud ML certifications

Tools

Git

Kubeflow

MLflow

SageMaker

dbt

InfluxDB

TimescaleDB

Job description

Ingersoll Rand is committed to achieving workforce diversity reflective of our communities. We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.

Job Title

Data Scientist – MLOps & Analytics Governance

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 driven by an ownership mindset and entrepreneurial spirit, has been a beacon of innovation for over 19 years, embodying our purpose to “Make Life Better” for our employees, customers, shareholders and the planet.

Job & Division 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. The ideal candidate is highly proficient in writing optimised, scalable Python and SQL code with deep hands‑on experience running large‑scale workloads in BigQuery on GCP. This role is critical to ensuring ML models are deployed reliably in cloud and that all insights reaching stakeholders are statistically sound and validated. The candidate is expected to actively leverage Generative AI tools (such as GitHub Copilot, Claude, or equivalent LLM‑based assistants) to accelerate software development, automate repetitive coding tasks, and improve overall engineering productivity. Domain exposure to manufacturing, IoT analytics, or rotating equipment such as air compressors is a strong advantage.

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 from training data through to production artifacts.
• Define and enforce data quality governance standards across all ML feature pipelines and training datasets – including schema contracts, null checks, range validation, and detection of training‑serving skew.
• Validate model outputs and analytical findings for statistical soundness and insights validation – reviewing for data leakage, biased evaluations, distributional assumptions, and reproducibility before results reach stakeholders.
• Set up model monitoring to track prediction drift, data drift, and performance degradation in production, and trigger automated retraining workflows when thresholds are breached.
• Work with large‑scale IoT sensor datasets from industrial equipment such as air compressors and rotating machinery 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, and clearly communicate model performance and business impact to technical and non‑technical stakeholders. 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 – writing and reviewing optimised, cost‑efficient SQL including partitioning, clustering, query plan analysis, and scalable transformation design for large‑scale workloads.
• Strong Python skills for writing and reviewing production‑grade ML code – feature engineering, batch scoring, and inference pipelines using scikit‑learn, TensorFlow, PyTorch, or Pandas. Proficient in using Gen AI coding assistants (GitHub Copilot, Claude, or similar) to boost development velocity and code quality.
• Hands‑on experience implementing data quality governance – schema contracts, automated profiling, pipeline‑level validation, lineage tracking, and quality scorecards integrated into ML workflows.
• Proven ability to perform insights validation – identifying data leakage, biased model evaluations, distributional shifts, and statistically unsound conclusions prior to stakeholder delivery.
• Strong grounding in statistical modeling – regression, classification, time‑series forecasting, hypothesis testing, and model behaviour under distributional shift.
• Familiarity with IoT data architectures – streaming pipelines, time‑series databases (InfluxDB, TimescaleDB), and high‑frequency sensor data processing at scale.
• Experience with version control (Git), code review workflows, and working in agile cross‑functional teams alongside data engineers and product managers.

Desired Skills

• Domain knowledge in air compressor systems, rotating equipment, or industrial machinery – understanding of operational parameters such as vibration, pressure, temperature, and flow rates.
• Exposure to predictive maintenance frameworks and condition‑based monitoring in a manufacturing or heavy‑industry environment.
• Experience with dbt or similar frameworks for scalable, tested, and documented SQL transformations in BigQuery.
• Familiarity with industrial IoT protocols such as MQTT and OPC‑UA, and cloud IoT ingestion services on GCP or AWS.
• Hands‑on experience with Generative AI tools for software development – using LLM‑based coding assistants (GitHub Copilot, Claude, Cursor, or equivalent) for code generation, automated test writing, SQL optimisation, and documentation; ability to critically review AI‑generated code for correctness, security, and performance before merging into production pipelines.

Basic Qualifications

• B.Tech / M.Tech – Computer Science or Data Science or Artificial Intelligence or Electrical / Mechanical Engineering.
• Certifications in MLOps or cloud ML (Google Professional ML Engineer, AWS ML Specialty) are a plus.

What we Offer

• We are all owners of the company! Stock options (Employee Ownership Program) that align your interests with the company’s success.
• Yearly performance‑based bonus, rewarding your hard work and dedication.
• Leave Encashments
• Maternity/Paternity Leaves
• Employee Health covered under Medical, Group Term Life & Accident Insurance
• Employee Assistance Program
• Employee development with LinkedIn Learning
• Employee recognition via Awardco
• Collaborative, multicultural work environment with a team of dedicated professionals, fostering innovation and teamwork.

Special Accommodation

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