Data Scientist - Machine Learning

Job Description :

The ideal candidate is a hands-on technology developer with experience in developing scalable applications and platforms. They must be at ease working in an agile environment with little supervision. The person should be a self-motivated person with a passion for problem-solving and continuous learning.

Designation :

Project Tech. Lead (4A) : 5 - 7 Years

Project Manager/Architect (4B/5A) : 7 - 10 Years

Role and responsibilities :

- Project Management (50%)

- Front Door (Requirements, Metadata collection, classification & security clearance)

- Data pipeline template development

- Data pipeline Monitoring development & support (operations)

- Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards.

- Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.

- Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.

Technical skills requirements :

The candidate must demonstrate proficiency in :

- Experience deploying machine learning models into a production environment.

- Strong DevOps, Data Engineering and ML background with Cloud platforms

- Experience in containerization and orchestration (such as Docker, Kubernetes)

- Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open-source frameworks.

- Experience building/operating systems for data extraction, ingestion and processing of large data sets

- Experience with MLOps tools such as MLFlow and Kubeflow

- Experience in Python scripting

- Experience with CI/CD

- Fluency in Python data tools e.g. Pandas, Dask, or Pyspark

- Experience working on a large scale, distributed systems

- Python/Scala for data pipelines

- Scala/Java/Python for micro-services and APIs

- HDP, Oracle skills & SQL; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC)

Nice-to-have skills :

- Jenkins, K8S

- Google Cloud certification

- Unix or Shell scripting