Hybrid Senior Lead Engineer – Data Analytics

at RTX

• Job title: Senior Lead Engineer – Data Analytics
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, PySpark
• Location requirements: Hybrid, Bengaluru, India

About the role

• Senior Lead Engineer developing physics based analytics for aircraft systems at Collins Aerospace. Collaborating with engineers and SMEs on predictive maintenance and anomaly detection models.

Responsibilities

• Develop physics based analytics & physics informed machine learning–based analytics for sensory data from aircraft systems (e.g., engines, actuators, fuel and air conditioning systems).
• Work closely with SMEs, design engineers, and PHM teams to understand the physical behavior of systems and translate it into analytical models.
• Analyze and process large-scale time-series data using Databricks, PySpark, and distributed computing frameworks.
• Develop fault detection, degradation tracking, and anomaly detection algorithms using statistical and ML methods.
• Integrate physics-based features (derived from thermodynamics, fluid mechanics, or system modeling) with ML models for improved interpretability and accuracy.
• Perform feature engineering, dimensionality reduction, and signal fusion across multiple sensors and systems.
• Validate and verify analytics using test data and SME feedback, ensuring physical consistency and robustness.
• Collaborate in the development of digital twins, prognostics and health management (PHM) models, and predictive maintenance frameworks.

Requirements

• Bachelors / Masters in Mechanical /Aeronautical / Electrical / Data Science
• Well versed with PHM (Prognostic Health Monitoring) concepts of feature engineering, anomaly detection, model performance metrics, signal processing, statistical analysis
• Knowledge on Physics based analytics & physics informed Machine Learning approaches
• Solid foundation in machine learning algorithms (regression, classification, anomaly detection, time-series forecasting).
• Experience developing physics-informed neural networks (PINNs) or hybrid physics + ML models (preferred)
• Knowledge of prognostics and health management (PHM), RUL (Remaining Useful Life) estimation, and system-level health modeling (preferred)
• Familiarity with simulation tools (MATLAB/Simulink, Modelica, NPSS) for validation and model correlation (preferred)
• Experience with digital twin frameworks or model-based system engineering (MBSE) environments (preferred)
• Exposure to cloud-based analytics, CI/CD pipelines, and MLflow for model management (preferred)

Benefits

• Meal coupons
• Group Term Life Insurance.
• Group Health Insurance.
• Group Personal Accident Insurance.
• Entitled for 18 days of vacation and 12 days of contingency leave annually.
• Employee scholar programme.
• Work life balance.
• Car lease programme.
• National Pension Scheme
• LTA
• Fuel & Maintenance /Driver wages