About the Role
Zeta Global is seeking an experienced Senior Data Scientist to join their Machine Learning team. In this role, you will be responsible for designing, building, and scaling machine learning solutions that drive marketing intelligence, campaign optimization, and business performance. This position covers the entire ML lifecycle, from data engineering and feature development to model deployment and performance monitoring, all within a cloud-based, distributed environment.
Reporting / Hours
1:00 PM – 10:00 PM IST (± 30 minutes flexibility)
Key Responsibilities
- Data Engineering & ETL
- Lead the design and development of scalable ETL pipelines across platforms such as Snowflake/Hive, Athena.
- Partner with cross-functional teams to optimize data architecture and workflows.
- Machine Learning & AI Development
- Design, develop, evaluate, and deploy ML/AI models in production environments.
- Build scalable ML pipelines for training, validation, deployment, and scoring.
- Develop advanced predictive models and recommendation engines.
- Optimize model performance through experimentation, feature engineering, and ensemble techniques.
- Implement automation frameworks to improve productivity and reproducibility.
- Cloud & Distributed Systems
- Leverage AWS technologies including EC2, EMR, S3, Airflow, and Athena.
- Build and maintain robust workflows in distributed computing environments.
- Analytics, Reporting & Marketing Automation
- Generate client-facing modeling insights dashboards and performance reports with a strong focus on accuracy, consistency, and explainability.
- Support marketing automation initiatives using Python, Snowflake, Apache Superset, and related technologies.
- Collaboration & Delivery
- Work on multiple concurrent projects in a fast-paced, high-growth environment.
- Communicate complex findings clearly to both technical and non-technical stakeholders.
- Mentor junior team members and contribute to best practices across the data science function.
Required Skills
- Strong expertise in supervised learning and advanced ML techniques (Random Forest, GBM, regression, neural networks, boosting and bagging methods, ensemble models).
- Proven experience building and productionizing ML pipelines.
- Advanced Python proficiency and experience with ML libraries.
- Strong data preparation and feature engineering skills.
- Experience with Snowflake and distributed data systems.
- Working knowledge of Unix and shell scripting.
- Strong analytical thinking, attention to detail, and communication skills.
Qualifications
- MSc/MS in a quantitative field with 4+ years of experience.
- OR
- BSc with 7+ years of relevant experience.