Senior 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

Senior Data Scientist

Location

Bangalore

About Us

Ingersoll Rand is a global provider of mission-critical flow creation life science and industrial solutions. Ingersoll Rands 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 Rands purpose and strategic focus areas.

Job & Division Summary:

We are looking for a technically strong Senior Analytics Engineer / Data Scientist to own both sides of a modern analytics and ML platform: Designing and rigorously reviewing SQL-based data pipelines and business insight logic on Google BigQuery and owning the end-to-end MLOps lifecycle for models running in production on GCP. This role blends hands-on engineering with technical review responsibility. You will build features ship models and hold the line on data correctness cost architecture and statistical soundness as the platform scales across multiple product lines and regions. The candidate is expected to actively leverage Generative AI tools (such as GitHub Copilot Claude or equivalent LLM-based assistants) to accelerate development and improve engineering productivity while remaining accountable for critically reviewing any AI-generated code or analysis before it reaches production or stakeholders. Domain exposure to industrial IoT equipment telemetry (e.g. air compressors rotating machinery) or sales data is a strong advantage.

Responsibilities

• Design and implement BigQuery SQL procedures views and table functions for business insight generation (e.g. anomaly detection usage trend evaluation equipment/IoT telemetry analysis).

• Analyze and optimize BigQuery query for cost and performance slot-seconds bytes scanned/shuffled join fan-out and enforce shared architectural conventions.

• 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.

• Set up model monitoring to track prediction drift data drift and performance degradation in production and build time-series and fault-detection pipelines for large-scale industrial IoT sensor data.

• Conduct evidence-based code reviews validating logic against live production data not just reading code and quantifying real impact (row counts cost deltas population sizes).

• 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.

• Collaborate with data engineers domain experts and product managers to translate ambiguous requirements into precise technically sound designs escalating unresolved product decisions to the right stakeholder and document findings and rationale clearly for asynchronous cross-functional review.

Mandatory Skills

• Strong hands-on experience with BigQuery (or another major cloud data warehouse) and advanced SQL CTEs window functions procedural SQL dry-run/cost analysis.

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

• Strong Python skills for production-grade ML code feature engineering batch scoring and inference pipelines using scikit-learn TensorFlow PyTorch or Pandas.

• Demonstrated ability to review code by verifying against real data not just static reading comfortable running exploratory queries to confirm or falsify a hypothesis.

• Hands-on experience implementing data quality governance schema contracts automated profiling pipeline-level validation and lineage tracking.

• 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 behavior under distributional shift.

• Comfortable working cross-functionally with product managers and business stakeholders to resolve ambiguous requirements with excellent written communication for documenting decisions and rationale.

• Experience with version control (Git) code review workflows and working in agile cross-functional teams.

Desired Skills

• Experience defining or maintaining internal engineering conventions/style guides for a shared codebase or with GitLab/GitHub-based structured review workflows.

• Familiarity with insight/anomaly-detection frameworks or similar rules-engine-style systems.

• 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.

• 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 optimization and documentation; ability to critically review AI-generated code for correctness security and performance before merging into production pipelines.

Education & Experience

• / Computer Science or Data Science or Artificial Intelligence from top tier colleges.
• Level: Senior 5 to 8 years of overall experience including 3 years of hands-on BigQuery/SQL-at-scale work and meaningful exposure to production ML systems.

What we Offer

• We are all owners of the company! Stock options (Employee Ownership Program)that align your interests with the companys 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 .