We are seeking a highly skilled and motivated Lead DS/ML engineer to join our team. The role is critical to the development of a cutting-edge reporting platform designed to measure and optimize online marketing campaigns.
We are seeking a highly skilled Data Scientist / ML Engineer with a strong foundation in data engineering (ELT, data pipelines) and advanced machine learning to develop and deploy sophisticated models. The role focuses on building scalable data pipelines, developing ML models, and deploying solutions in production to support a cutting-edge reporting, insights, and recommendations platform for measuring and optimizing online marketing campaigns.
The ideal candidate should be comfortable working across data engineering, ML model lifecycle, and cloud-native technologies.
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).
