Ingersoll Rand - Senior Data Scientist

Job 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.

Responsibilities:

- Design and implement BigQuery SQL procedures, views, and table functions for business insight generation.

- Analyze and optimize BigQuery query for cost and performance 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.

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

- Conduct evidence-based code reviews - validating logic against live production data.

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

- Validate model outputs and analytical findings for statistical soundness.

- Collaborate with data engineers, domain experts, and product managers to translate ambiguous requirements into precise designs.

Mandatory Skills:

- Strong hands-on experience with BigQuery and advanced SQL.

- Deep ownership of MLOps - CI/CD for ML, model versioning, deployment automation, and drift monitoring on GCP or AWS.

- Strong Python skills for production-grade ML code.

- Demonstrated ability to review code by verifying against real data.

- Hands-on experience implementing data quality governance.

- Proven ability to perform insights validation.

- Strong grounding in statistical modeling.

- Excellent written communication for documenting decisions.

- Experience with version control (Git) and agile workflows.

Education & Experience:

- B.Tech / M.Tech.

- 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.