- 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 solid 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Data Scientist (Bengaluru)
🏢 Ingersoll Rand
📍 Bengaluru