Key Responsibilities:

1. Data Engineering & Pipeline Development

• Design, build, and maintain scalable ELT pipelines for ingesting, transforming, and processing large-scale marketing campaign data.
• Ensure high data quality, integrity, and governance using orchestration tools like Apache Airflow, Google Cloud Composer, or Prefect.
• Optimize data storage, retrieval, and processing using BigQuery, Dataflow, and Spark for both batch and real-time workloads.
• Implement data modeling and feature engineering for ML use cases.

2. Machine Learning Model Development & Validation

• Develop and validate predictive and prescriptive ML models to enhance marketing campaign measurement and optimization.
• Experiment with different algorithms (regression, classification, clustering, reinforcement learning) to drive insights and recommendations.
• Leverage NLP, time-series forecasting, and causal inference models to improve campaign attribution and performance analysis.
• Optimize models for scalability, efficiency, and interpretability.

3. MLOps & Model Deployment

• Deploy and monitor ML models in production using tools such as Vertex AI, MLflow, Kubeflow, or TensorFlow Serving.
• Implement CI/CD pipelines for ML models, ensuring seamless updates and retraining.
• Develop real-time inference solutions and integrate ML models into BI dashboards and reporting platforms.

4. Cloud & Infrastructure Optimization

• Design cloud-native data processing solutions on Google Cloud Platform (GCP), leveraging services such as BigQuery, Cloud Storage, Cloud Functions, Pub/Sub, and Dataflow.
• Work on containerized deployment (Docker, Kubernetes) for scalable model inference.
• Implement cost-efficient, serverless data solutions where applicable.

5. Business Impact & Cross-functional Collaboration

• Work closely with data analysts, marketing teams, and software engineers to align ML and data solutions with business objectives.
• Translate complex model insights into actionable business recommendations.
• Present findings and performance metrics to both technical and non-technical stakeholders.

Qualifications & Skills:

Educational Qualifications:

 - Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
 - Certifications in Google Cloud (Professional Data Engineer, ML Engineer) is a plus.

Must-Have Skills:

 - Experience: 5-10 years with the mentioned skillset & relevant hands-on experience

 - Data Engineering: Experience with ETL/ELT pipelines, data ingestion, transformation, and orchestration (Airflow, Dataflow, Composer).
 - ML Model Development: Strong grasp of statistical modeling, supervised/unsupervised learning, time-series forecasting, and NLP.
 - Programming: Proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and SQL for large-scale data processing.
 - Cloud & Infrastructure: Expertise in GCP (BigQuery, Vertex AI, Dataflow, Pub/Sub, Cloud Storage) or equivalent cloud platforms.
 - MLOps & Deployment: Hands-on experience with CI/CD pipelines, model monitoring, and version control (MLflow, Kubeflow, Vertex AI, or similar tools).
 - Data Warehousing & Real-time Processing: Strong knowledge of modern data platforms for batch and streaming data processing.

Nice-to-Have Skills:

 - Experience with Graph ML, reinforcement learning, or causal inference modeling.
 - Working knowledge of BI tools (Looker, Tableau, Power BI) for integrating ML insights into dashboards.
 - Familiarity with marketing analytics, attribution modeling, and A/B testing methodologies.
 - Experience with distributed computing frameworks (Spark, Dask, Ray).