Lead Data Engineer - Artificial Intelligence/Machine Learning

Description :

Duties & Responsibilities :

- 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 :

- 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.