Senior Data Scientist

We are seeking a Senior Data Scientist to develop hierarchical time series forecasting models to predict revenue trends across multiple levels (overall, by category, by BC number). In this role, you will apply advanced forecasting techniques (ARIMA, SARIMAX, XGBoost, MLforecast) and tackle complex data challenges. You will be responsible for end-to-end model development, including data analysis, exploratory data analysis (EDA), feature engineering, supervised learning, classification models, deployment, and ML engineering. Additionally, you will provide data science consulting, guide business strategies, and handle complex debugging issues.

Key Responsibilities

• Time Series Forecasting & Data Analysis:
  
  • Build and deploy hierarchical time series models to predict revenue at multiple levels (overall, category, BC number).
    
  • Apply ARIMA, SARIMAX, and MLforecast for modelling seasonality, trends, and patterns in financial data.
    
  • Perform exploratory data analysis (EDA) to identify key trends, outliers, and patterns.
    
  • Feature Engineering & Supervised Learning:
    
    • Conduct feature engineering, creating meaningful features for model input using domain knowledge and statistical techniques.
      
    • Apply supervised learning techniques, including XGBoost, Random Forest, and other regression/classification models, to enhance forecasting accuracy.
      
    • Problem-Solving & Debugging:
      
      • Tackle complex data science challenges and debugging issues related to model performance, identifying root causes, and providing effective solutions.
        
      • Optimize models to improve prediction accuracy and performance.
        
      • Data Science Consulting & Collaboration:
        
        • Work closely with cross-functional teams to align forecasting models with business objectives.
          
        • Provide data science consulting and strategic guidance, translating complex analytical insights into actionable business strategies.
          
        • Communicate technical results clearly to non-technical stakeholders through visualizations and reports.
          
        • ML Engineering & Deployment:
          
          • Develop scalable, production-ready models in Databricks and other cloud-based platforms.
            
          • Deploy machine learning models, ensuring they are optimized for performance and integrated into existing business processes.
            
          • Use MLflow and other deployment tools for tracking experiments, version control, and model deployment.
            
          • Model Optimization & Continuous Improvement:
            
            • Continuously refine and improve forecasting models, experimenting with new techniques and tools.
              
            • Monitor model performance in production and implement changes to enhance forecasting accuracy.
              
              
              

              Required Skills & Experience
              

              • 5+ years of hands-on experience in data science, with a focus on time series forecasting, supervised learning, and financial data modeling.
                
              • Expertise in Python and machine learning libraries such as Pandas, Scikit-learn, XGBoost, MLforecast, and Statsmodels.
                
              • Strong experience in ARIMA, SARIMAX, and other time series forecasting techniques.
                
              • Proficiency in feature engineering and EDA to create insightful data transformations and model inputs.
                
              • Hands-on experience with supervised learning algorithms, including regression and classification models.
                
              • Experience in ML engineering, deploying models at scale using Databricks, MLflow, and cloud platforms.
                
              • Expertise in data visualization tools such as Matplotlib, Seaborn, and Plotly.
                
              • Strong understanding of financial metrics and revenue forecasting.
                
              • Excellent problem-solving and debugging skills, particularly for complex model and data issues.
                
                
                

                Preferred Qualifications
                

                • Experience with deep learning (e.g., LSTM, RNN) for time series forecasting.
                  
                • Experienced graduates/Masteror Ph.D. in Data Science, Computer Science, Statistics, or related fields.
                  
                • Prior experience in data science consulting, providing strategic insights and guidance to business teams.