Data Scientist

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

Location  

Bangalore

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, A GREAT PLACE TO WORK CERTIFIED WORKPLACE 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.  

The Engineering & Technology center has expertly supported a diverse range of industrial products, offering deep expertise in core and digital engineering space. By cultivating a sense of inclusion, belonging and respect, and a collaborative culture, the GEC has fostered the most talented and capable engineers, thereby playing a pivotal role in driving Ingersoll Rand’s purpose and strategic focus areas. 

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.   

Ingersoll Rand Inc. (NYSE:IR), driven by an entrepreneurial spirit and ownership mindset, is dedicated to helping make life better for our employees, customers and communities. Customers lean on us for our technology-driven excellence in mission-critical flow creation and industrial solutions across 40+ respected brands where our products and services excel in the most complex and harsh conditions. Our employees develop customers for life through their daily commitment to expertise, productivity and efficiency. For more information, visit www.IRCO.com.