Assistant General Manager- AI/ML Technologies

1. BASIC ROLE DETAILS
Position Title Senior Data Scientist – AI/ML Technologies Grade: JE is to be done
Business Group / Division/ Department Research & Development
Location Thane (To align with the Head's location)
This Role Reports to Head of AI/ML Technologies
This Role Supervises Junior Data Scientists & ML Engineers

2. PURPOSE OF THE ROLE
Senior Data Scientist with a strong practical background in deploying production-ready AI/ML solutions. This role focuses on developing advanced agentic AI systems, time series analytics, and signal processing capabilities to optimize our refrigeration solution technologies, HVAC systems, and electronics platforms.
Role Position in the Organization (enclose a copy of the organization chart)


3. IDEAL PROFILE
Educational Qualification Essential Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or related field
Preferred Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or related field
Total Experience Required 7+ Yrs Relevant Experience 7+ Yrs
Functional / Technical Expertise • Agentic AI & Machine Learning: Hands-on experience building agentic AI systems or autonomous decision-making algorithms. Knowledge of reinforcement learning, multi-agent systems, or autonomous optimization frameworks. Exposure to LLM-based agents, tool use, or reasoning frameworks for decision-making. Solid understanding of supervised and unsupervised ML algorithms with deployment experience.

• Time Series Analysis: Experience with time series forecasting (ARIMA, Prophet, LSTM). Hands-on work with seasonal patterns, trend analysis, time series decomposition, and applying techniques to real-world datasets (sensor data, energy consumption). Familiarity with handling missing data, outliers, and non-stationary time series.
• Signal Processing: Working knowledge of digital signal processing (filtering, FFT, spectral analysis). Experience processing sensor data from industrial equipment (vibration, temperature, pressure, acoustic signals). Ability to implement feature extraction and noise reduction techniques. Understanding of frequency domain analysis.
• Programming & Tools: Strong proficiency in Python with ML libraries (scikit-learn, TensorFlow or PyTorch, XGBoost). Experience with signal processing (scipy.signal, PyWavelets) and time series libraries (statsmodels, Prophet, or tslearn). Experience with at least one cloud platform (Azure preferred, AWS, or GCP). Solid SQL skills and familiarity with data streaming (Kafka, MQTT). Version control with Git and basic MLOps practices.

Preferred expertise

• Azure Machine Learning: Experience with Azure Machine Learning workspace, automated ML, deployment capabilities, Azure ML pipelines, and model registry. Exposure to Azure Databricks, Azure Synapse Analytics, or Azure IoT Hub. Basic knowledge of Azure DevOps for CI/CD.
• Genetic AI/Evolutionary Algorithms: Exposure to genetic algorithms or evolutionary strategies for optimization problems (hyperparameter tuning or feature selection).
• Predictive Maintenance: Experience contributing to predictive maintenance projects, failure prediction models, remaining useful life (RUL) estimation, and condition-based monitoring concepts.

Behavioral / Leadership Competency • Stakeholder Management: Collaborating with engineering and operations teams.

• Strategic Thinking & Execution
• Program & Project Leadership
• Mentoring
• Collaboration & Networking
• Self-Leadership: Demonstrates high self-drive, being a self-starter
• Strong Communication

MAIN RESPONSIBILITIES
Key Deliverables for this position

• Design and deploy agentic AI systems that autonomously optimize HVAC&R solutions’ operations, energy consumption, and equipment performance.
• Develop and implement advanced time series forecasting models for energy demand, equipment behavior, and operational patterns.
• Apply signal processing techniques to analyze sensor data, detect anomalies, and extract meaningful patterns from noisy industrial environments.
• Build end-to-end machine learning pipelines from data ingestion through model deployment and monitoring in production systems.
• Lead predictive maintenance initiatives using ML models to forecast equipment failures and optimize maintenance schedules.
• Collaborate with engineering and operations teams to translate business problems into practical data science solutions.
• Mentor junior data scientists and establish best practices for model development and deployment.

Key Performance Indicators

• Model Deployment: Number and impact of production-ready AI/ML solutions deployed.
• Optimization Value: Measurable impact on building operations, energy consumption, and equipment performance/uptime achieved via AI/ML.
• Team/Process Maturity: Effectiveness in mentoring and establishing best practices for model development and deployment.

5.
KEY INTERACTIONS
With Whom Brief Description
External • External consultants, if deployed

• Project consultants
• Software providers

Internal • Program & Project managers

• System integrators
• Electronics & testing engineers

6. WORKING CONDITIONS (PHYSICAL & ENVIRONMENTAL DEMAND)
Office based environment and Regular travel to various R&D office locations and factories

7. AUTHORITY - INDICATE THE APPROVAL AUTHORITY FOR THIS POSITION, IF ANY.
People
Financial

8. ACCOUNTABILITY & CHALLENGES
Key Financial Accountabilities • •
Major Challenges • Translating business problems into practical data science solutions and delivering production-ready, measurable models.

• Coordinating with cross-functional teams (System Integration, Quality, Electronics) for the smooth release of complex, integrated products.
• Maintaining data Integrity for strategic decision-making