RESPONSIBILITIES:
Machine Learning & Forecasting
- Design and implement forecasting models for room occupancy, addressing both short-term and long-term demand patterns.
- Build and orchestrate end-to-end machine learning workflows on Databricks, from feature engineering to model training and evaluation.
- Apply and experiment with time-series models, including LSTM (Long Short-Term Memory) and other deep learning approaches.
- Address current challenges such as model overfitting and selection of meaningful data signals beyond historical trends.
- Enable forecasting at different market segment levels (e.g., class of customers, demand segments).
- Engineer robust processes to select, version, and promote the best-performing models into production (MLOps).
Data & Platform Engineering (Azure / Databricks)
- Review, correct, and strengthen the existing Azure and Databricks infrastructure.
- Implement scalable, maintainable ML pipelines using Databricks, PySpark, and Python.
- Apply software and ML design patterns to improve code quality, reusability, and long-term maintainability.
- Collaborate with data engineers to ensure high-quality, reliable, and well-governed data pipelines.
Dashboarding & Web Application (High Priority)
- Design and develop a Django-based dashboard to replace or significantly reduce reliance on Power BI licenses.
- Build an interactive, user-friendly web application that clearly showcases forecasting outputs and insights.
- Implement the visualization layer using Plotly (or similar Python visualization frameworks).
- Where required, embed Power BI dashboards selectively while transitioning to a Django-native visualization approach.
- Ensure the dashboard becomes the primary value demonstration layer for business stakeholders.
GenAI & Advanced Analytics
- Leverage Generative AI techniques to surface insights, explanations, and trends from forecasting results.
- Enhance interpretability and storytelling around predictions for business users.
PROFILE
- Strong experience building Django-based web applications (dashboarding is a top priority).
- Hands-on experience with Databricks for data processing and machine learning workflows.
- Solid expertise in Python for ML, data engineering, and backend development.
- Strong software engineering fundamentals (clean code, modular design, testing, version control).
- Experience with interactive data visualization using Plotly or equivalent libraries.
- Proven experience in time-series forecasting and demand prediction use cases.
- Practical experience with deep learning models, especially LSTM or similar architectures.
- Strong understanding of overfitting, feature selection, and signal extraction in real-world data.
- Experience operationalizing ML models (model selection, deployment, monitoring).
- Experience with Azure cloud services supporting data and ML platforms.
- Exposure to MLOps practices (CI/CD for ML, model versioning, monitoring).
- Experience applying GenAI for analytics, insight generation, or decision support.
- Prior experience replacing or modernizing Power BI-heavy reporting landscapes.
- Strong delivery-oriented mindset with the ability to demonstrate value quickly.
- Comfortable owning both backend ML pipelines and frontend dashboarding.
- Able to translate complex forecasting outputs into clear, actionable insights for business users.
- Strong collaboration skills with data engineers, architects, and business stakeholders.
