Data Scientist

**Job Requirements**:

- 5+ years of strong experience in building & maintaining machine & deep learning models
- Experience in performing detailed exploratory data analysis (EDA) & having sound statistical knowledge
- Understanding of concepts related to algorithm and deep learning architecture
- Develop prototypes, proof of concepts, algorithms, predictive models, and custom analysis
- Analysis of structured and unstructured data from problem statement to model deployment & business impact using ML/DL/AI
- Should be hands on AWS cloud services that includes VPC, EC2, S3, RDS, Redshift, Data Pipeline, EMR, DynamoDB, DevOps, Lambda, Kinesis, DMS, SNS, SQS, Sagemaker, Apache Airflow, and EKS.
- Proficient in big data ingestion and streaming tools like Amazon Kinesis, Beam or Kafka
- Experience in deploying models via APIs and on the edge devices in a secure manner.
- Good knowledge on various scripting languages like Linux/Unix shell scripting and Python
- Good knowledge of Datawarehousing concepts and ETL processes using Redshift & SQL Server
- Experienced in using IDEs and Tools like GitHub, AWS CodeCommit, Jupyter Notebooks/Lab and Anaconda.
- Strong team player, ability to work independently and in a team as well, ability to adapt to a rapidly changing environment, commitment towards learning, & documentation skills
- Sound practical knowledge of statistical packages like - R & Python
- Creating data visualisations to effectively convey findings using Tableau.

**Technical Skills**:

- Cloud Platform: AWS
- Big Data: Python, Pytorch, Torchserve, Airflow, TensorFlow
- Databases: Redshift and SQL Server.
- Visualization: Tableau or Quicksight
- Languages: SQL, TensorFlow, Pytorch and Python
- Tools/IDE: Docker, Kubernetes, Airflow, Jupyter Notebooks/Labs and Anaconda
- Operating Systems: UNIX, LINUX
- AWS Services: EC2, Sagemaker, RDS, Lambda, Redshift, S3, CloudWatch.
- Packages: MS Office Suite
- DevOps: AWS Code commit
- ML Ops
- Other Tools: Putty