Data Scientist

Job Summary

5-8 years of professional experience

Roles And Responsibilities

• Analyze large, complex datasets from various building devices (alarms, notifications, sensor data) to identify patterns, anomalies, and opportunities for operational optimization.
• Design, develop, and deploy machine learning and deep learning models to predict equipment failures, optimize energy consumption, and identify unusual operational behavior.
• Develop and implement agentic workflows to automate decision-making processes and trigger intelligent actions based on real-time data insights.
• Explore and integrate Large Language Models (LLMs) to interpret unstructured data (e.g., maintenance logs, technician notes) and generate natural language insights or automate reporting.
• Collaborate with Data Engineers to define data requirements, ensure data quality, and optimize data pipelines for machine learning applications.
• Work closely with Software Engineers to integrate developed models and intelligent agents into the React frontend and Node.js backend of the SmartFM platform.
• Evaluate and monitor the performance of deployed models, implementing strategies for continuous improvement and retraining.
• Communicate complex analytical findings and model insights clearly and concisely to technical and non-technical stakeholders.
• Stay abreast of the latest advancements in AI, ML, Deep Learning, Agentic AI, and LLMs, assessing their applicability to facility management challenges and advocating for their adoption.
• Contribute to the strategic roadmap for AI/ML capabilities within the SmartFM product.
  

Required Technical Skills And Experience

• 5-8 years of professional experience in Data Science, Machine Learning Engineering, or a related analytical role.
• Strong proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch).
• Proven experience in developing and deploying Machine Learning models for predictive analytics, anomaly detection, and classification problems.
• Hands-on experience with Deep Learning frameworks and architectures for time series analysis, pattern recognition, or natural language processing.
• Demonstrated experience in designing and implementing Agentic Workflows or intelligent automation solutions.
• Practical experience working with Large Language Models (LLMs), including fine-tuning, prompt engineering, or integrating LLM APIs for specific use cases.
• Solid understanding of statistical modeling, experimental design, and A/B testing.
• Experience with querying and analyzing data from MongoDB and working with streaming data from Kafka.
• Familiarity with data ingestion processes, ideally involving IBM StreamSets.
• Experience with cloud-based ML platforms and services (e.g., AWS SageMaker, Azure ML, Google AI Platform).
  

Additional Qualifications

• Proven expertise in written and verbal communication, adept at simplifying complex technical concepts for both technical and non-technical audiences.
• Strong problem-solving and analytical skills with a passion for extracting insights from data.
• Experienced in collaborating and communicating seamlessly with diverse technology roles, including data engineering, software development, and product management.
• Highly motivated to acquire new skills, explore emerging technologies, and stay updated on the latest trends in AI/ML and business needs.
• Domain knowledge in facility management, IoT, or building automation is a significant advantage.
  

Education Requirements / Experience

• Master’s or Ph.D. degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related quantitative field.