Data Science Engineer - ML
DATAECONOMY · Gurgaon, Haryana, India
Information Technology & Services · 201-500 employees
About the role
Design and implement cloud-based MLOps solutions while building robust data pipelines for data scientists and engineers. Assist in model review, code optimization, containerization, and the deployment and monitoring of machine learning models.
What they look for
Requirements
Requires a bachelor's or master's degree in computer science or a related field with at least 5 years of professional software development experience. Candidates must have strong proficiency in Python, R, Spark, and extensive hands-on experience with AWS SageMaker and cloud data technologies.
Full description
Position: Data Science Engineer – Data
BASIC QUALIFICATIONS
- Bachelor's
or master’s degree in computer science, IT or related technical field
· 5+ years of professional software development experience
· 3+ year experience with programming languages such as Python, R and open-source technologies (Apache, Hadoop, Spark, PyTorch, TensorFlow)
PREFERRED QUALIFICATIONS
- Proficiency
in Python, R, Spark.
- Machine
learning knowledge and experience.
- Experience
building tools for data scientists and developers. Must have experience in AWS SageMaker and AWS SageMaker Studio
- Experience
with IDE/notebook software (Jupyter Studio, R-Studio, VSCode, PyCharm, etc)
- Experience
in building data pipeline using on cloud using Cloud technologies (S3, Lakeformation, SQS, SNS, Kinesis, Spark, Kafka, Glue etc)
- Good
to have experience in Data Visualization tools like SAS, Tableau, AWS Quicksight
Key Responsibilities:
- Design
and implement cloud solutions, build MLOps on cloud. Preferably AWS Cloud.
- Build
model and data pipelines for Data Scientists and Data Engineers using AWS cloud services.
- Assist
in data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its model quality.
- Hands
on experience with different features of AWS SageMaker including but not limited to SageMaker Studio, Jupyter Notebooks, Data Wrangler, Clarify etc.
- Good
understanding of the Python ML Libraries. Should be able to prototype and evaluate new libraries and new features available.
- Experience
in communicating with Data science team, Cloud Infrastructure team and developers to collect requirements, describe software product features, and technical designs.
- Ability
and willingness to multi-task and learn new technologies quickly
- Stakeholder
management with good Written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences
