Data Scientist - Remote

- Conduct advanced time-series analysis including stationarity testing, decomposition, autocorrelation, seasonality modelling, and change-point detection.
- Analyse and integrate heterogeneous sensor datasets including Wi-Fi, IoT, BMS, mobile, occupancy, and utility systems.
- Design sensor-fusion and data-quality frameworks with validation, reliability scoring, reconciliation, and gap-filling strategies.
- Engineer forecasting and predictive features using temporal patterns, weather, occupancy, operational schedules, and cross-series correlations.
- Clean, preprocess, and normalise noisy sensor streams using outlier detection, imputation, smoothing, and resampling techniques.
- Build, benchmark, and evaluate forecasting models on Vertex AI using statistical, ML, and deep-learning approaches with rigorous back-testing methodologies.
- Develop and operationalise ML pipelines on Vertex AI, Cloud Run, and Cloud Scheduler, including drift monitoring, automated retraining, and lifecycle management.
- Implement anomaly-detection and alerting frameworks using statistical and machine-learning techniques, validated against field and manual measurements.
- Build AI-driven analytics and agentic workflows using Generative AI, Vertex AI Agent Builder, LangChain/LangGraph, RAG, and FastAPI for conversational insights and operational recommendations.
- Create Looker dashboards, technical documentation, operational runbooks, evaluation frameworks, and enablement materials to support forecasting operations and stakeholder adoption."

- Strong experience in time-series analysis, forecasting, and predictive modeling using statistical, machine learning, and deep learning techniques.
- Hands-on experience working with IoT, sensor, or operational datasets, including data integration, quality validation, and preprocessing.
- Proficiency in Python and modern ML/AI frameworks for data analytics, anomaly detection, and predictive solutions.
- Experience building and operationalizing ML pipelines on cloud platforms, preferably Google Cloud Platform (Vertex AI, Cloud Run, Cloud Scheduler).
- Solid understanding of Generative AI, RAG, Agentic AI, and frameworks such as LangChain, LangGraph, FastAPI, or similar technologies.
- Experience implementing model monitoring, drift detection, automated retraining, and ML lifecycle management in production environments.
- Strong communication and documentation skills, with the ability to present insights and recommendations to both technical and business stakeholders.

Nice to Have
- Experience in smart building, energy management, facility management, or occupancy analytics domains.
- Familiarity with Looker, BigQuery, and broader Google Cloud ecosystem services.
- Experience building conversational AI assistants or AI-powered operational recommendation systems.
- Knowledge of MLOps best practices, automated retraining, model governance, and lifecycle management.
- Strong English communication skills with the ability to work directly with international stakeholders.

- Receive 100% salary from the onboarding date.
- Participate in company activities: Teambuilding, travel, vacation and other activities.
- Work with large and advanced systems, have the opportunity to develop comprehensive technology skills with complex problems, requiring high accuracy.