Lead Data Engineer - Artificial Intelligence/Machine Learning

Description Duties & Responsibilities & Modeling : • 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. AI System Design • 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. Data Engineering & APIs • 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. Preferred Qualifications • 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)