Data Scientist - Machine/Deep Learning (4-12 yrs)

Desired Competencies (Technical/Behavioral Competency) :

Must-Have :

- 4+ years of experience in Data Science, ML & DL.

- Well versed with building Algorithms to predict Network Performance degradation.

- Expertise in Acumos AI and MLFlow which are platforms to manage and package ML lifecycle.

- Microservices, Cloud native technologies, Serverless Computing

- Cloud computing, SDN, NFV

- Docker, Kubernetes, Redhat Openshift, Kubeflow

- Strong development skills including Linux, Shell, Python, Java, Scala

Good-to-Have :

- Exposure to GCP, AWS and Azure services.

- Exposure to Cloud Native Tools: Docker,Kubernetes, Jaeger, Fluentd, Elastic Search, Kibana, Istio, Harbor, Prometheus, Grafana, Ansible, Git, Jenkins, Gerrit, Knative

Development of Multi-Processor Performance Benchmarking (MPPB),a solution to optimize performance of various workloads (AI, NFV, Database, and Webservers) by tuning the processor configurations.

- Development of Microservices Development and Deployment Platform(MDDP) a platform that aids in the development and deployment of microservices with Kubernetes, OpenShift as underlying orchestrators.

- Development of Ascent, a framework for upgrading microservice based applications in targeted regions using Istio and Knative.

- Develop machine learning model to predict the Network element (Switches or Routers) failure in near future from syslogs and network performance metrics data.

- Develop machine learning model for a client which needs to predict the downlink throughput in telecom. using the TensorFlow package using python.

- Develop an algorithm for radio network optimization, which suggests the radio network engineer to change the configuration of the base station.

- Based on learned patterns from the several base stations in the telecommunications.