Data Scientist - Machine Learning

<p><p><p>Job Description :<br/><br/>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.<br/><br/>Designation :<br/><br/>Project Tech.

Lead (4A) : 5 - 7 Years<br/><br/>Project Manager/Architect (4B/5A) : 7 - 10 Years<br/><br/>Role and responsibilities :<br/><br/>- Project Management (50%)<br/><br/>- Front Door (Requirements, Metadata collection, classification & security clearance)<br/><br/>- Data pipeline template development<br/><br/>- Data pipeline Monitoring development & support (operations)<br/><br/>- 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.<br/><br/>- Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.<br/><br/>- Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.<br/><br/>Technical skills requirements :<br/><br/>The candidate must demonstrate proficiency in :<br/><br/>- Experience deploying machine learning models into a production environment.<br/><br/>- Strong DevOps, Data Engineering and ML background with Cloud platforms<br/><br/>- Experience in containerization and orchestration (such as Docker, Kubernetes)<br/><br/>- Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open-source frameworks.<br/><br/>- Experience building/operating systems for data extraction, ingestion and processing of large data sets<br/><br/>- Experience with MLOps tools such as MLFlow and Kubeflow<br/><br/>- Experience in Python scripting<br/><br/>- Experience with CI/CD<br/><br/>- Fluency in Python data tools e.g. Pandas, Dask, or Pyspark<br/><br/>- Experience working on a large scale, distributed systems<br/><br/>- Python/Scala for data pipelines<br/><br/>- Scala/Java/Python for micro-services and APIs<br/><br/>- HDP, Oracle skills & SQL; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC)<br/><br/>Nice-to-have skills :<br/> <br/>- Jenkins, K8S<br/><br/>- Google Cloud certification<br/><br/>- Unix or Shell scripting</p></p></p> (ref:hirist.tech)