Marketing Data Scientist
3 days ago
Immediate to 15 Days
Experience: 8+ Years
We are seeking a highly experienced Data Scientist / Data Science Engineer to design, build, deploy, and maintain machine learning models and scalable production systems. This role focuses on bridging the gap between research and production by implementing MLOps best practices , ensuring model reliability, and optimizing performance at scale. You will collaborate with cross-functional teams including Data Scientists, Data Engineers, DevOps, and Product Managers to deliver enterprise-grade AI/ML solutions.
Model Development & Deployment
Develop, optimize, and deploy machine learning models into scalable and secure production environments.
Data Pipeline Engineering
Build and optimize data pipelines for model training, evaluation, and inference.
Ensure high data quality, availability, and consistency across the ML lifecycle.
Implement frameworks for model versioning, monitoring, retraining, and CI/CD.
Use platforms such as MLflow, Kubeflow, Airflow for end-to-end ML lifecycle management.
Cloud & Platform Engineering
Deploy ML solutions using cloud platforms ( AWS, Azure, GCP ) and their managed ML services.
Manage containerized workloads using Docker and Kubernetes .
System Reliability & Performance
Monitor production systems for data drift, performance degradation, and anomalies.
Optimize latency, throughput, and cost of ML services in production.
Collaboration & Agile Delivery
Work in Agile teams, participate in sprint planning, code reviews, and maintain clear documentation.
Partner with business and technical stakeholders to align ML solutions with business goals.
Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field.
Strong coding expertise in Python .
Hands-on experience with ML frameworks : scikit-learn, TensorFlow, PyTorch.
Proficiency in cloud platforms : AWS, Azure, or GCP.
Experience with Docker, Kubernetes for model deployment at scale.
Familiarity with data engineering tools (Spark, Kafka, SQL/NoSQL databases).
Competency in data visualization : Tableau, Power BI, matplotlib, seaborn.
Familiarity with Agile practices.
Experience with real-time or streaming ML systems .
Implemented CI/CD pipelines for ML models .
Knowledge of Responsible AI principles (fairness, explainability, bias mitigation).
Internal: Data Scientists, Data Engineers, DevOps, Software Engineers, Product Managers, Business Stakeholders.
Cloud Service Providers, Vendors, AI/ML Communities.
Choice of ML tools, frameworks, deployment strategies.
Budget Responsibility: Influence on cloud and ML tooling costs.
Geographic Scope: Global or regional project scope depending on business needs.
Number of ML models deployed successfully to production.
Reduced lead time for ML model deployment.
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