Lead Data Engineer - Artificial Intelligence/Machine Learning

Overview

• Develop and deploy time-series forecasting models (e.g., Prophet, ARIMA, DeepAR, LSTM, Temporal Fusion Transformer) to predict demand, revenue, and promotion lift.
• Apply advanced statistical and causal-inference methods to understand promotion effects and seasonality.
• Build dynamic pricing and promotion recommendation models leveraging reinforcement learning or multi armed bandits.
  
  

• Architect ML pipelines for promotion targeting and ROI optimization
• Design end to end data workflows from data ingestion to model deployment integrated with
  
  

production APIs and dashboards.

• Contribute to scalable model monitoring, retraining, and drift detection.
  
  

• Work with SQL databases, data warehouses (Azure SQL, Snowflake, Databricks Delta, etc.), and APIs to retrieve, transform, and operationalize data.
• Collaborate with cloud teams to ensure efficient and secure deployment of AI services (Azure
  
  

or GCP).

Requirements

Basic Qualifications :

• Masters degree in computer science, Data Science, Statistics, or a related field.
• 6+ years of experience in designing, developing, and deploying AI/ML solutions.
• Proficiency in Python and its AI/ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong expertise in forecasting and time series analysis techniques.
• Solid understanding of machine learning algorithms and statistical modeling.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Familiarity with version control systems (e.g., Git) and collaborative development workflows.
• Strong problem-solving and analytical skills.
  
  

• Ph.D. in Computer Science, Data Science, Statistics, or a related field.
• Experience with cloud platforms (e.g., Azure, GCP) for deploying AI solutions.
• Knowledge of advanced time series models (e.g., ARIMA, Prophet, LSTMs, Transformers).
• Expertise in handling large datasets and distributed computing frameworks (e.g., Spark, Dask).
• Familiarity with MLOps practices and tools for model deployment and monitoring.
• Strong understanding of statistical programming and data visualization tools (e.g., Matplotlib, Seaborn).
• Experience with database systems and query optimization.
• Excellent communication skills for cross-functional collaboration and presenting technical concepts.
  
  

(ref:hirist.tech)