Sr Data Science Engineer (Nuc Job/ 80)
Job Skills
Job Description
REQUIREMENT - Sr. Data Science Engineer
Experience- 3 to 6 Years
Availability- Immediate
Shift Timings- 1:30 PM to 10:30 PM
Mode - Remote
No of Positions - 1
Brief Job Description:
We are seeking a Data Science Engineer with over 3 years of experience to join our team. As a Data Science Engineer, you will leverage Python, AWS, and advanced statistical frameworks to drive our data- driven decision-making. You will be responsible for the end-to-end ML lifecycle—from deep-dive EDA and modeling in Google Meridian / scikit-learn to managing deployments in SageMaker and versioning with MLflow.
Key Responsibilities:
● End-to-End ML Development: Design, build, and deploy statistical models and machine learning algorithms using Python and AWS SageMaker.
● Insights Generation: Lead independent EDA phases to validate data integrity and extract pre- modeling insights that inform business strategy.
● Model Management: Implement model versioning and experiment tracking using MLflow to ensure reproducibility and performance monitoring.
● Collaboration & Code Quality: Participate in peer code reviews on GitHub, maintaining high standards for documentation and modular, scalable code.
● Measurement Support: Support the development of MMM and attribution frameworks, utilizing Bayesian methods where applicable to measure media impact.
● Documentation: Maintain technical documentation for data pipelines and model architectures to support collaborative growth.
Primary Skills Required
· Python Expertise: Strong proficiency in Python for backend and data applications. Expert proficiency in pandas and numpy for data manipulation; matplotlib, seaborn, and plotly for visualization.
· Data Processing: Hands-on experience with the pandas library for data manipulation, transformation, and analysis.
· Data Engineering: Develop Python-based automation for data ingestion, transformation, deduplication, and validation.Advanced experience in Exploratory Data Analysis (EDA), with the ability to independently assess data quality and surface insights.
· ETL/Data Pipelines: Strong knowledge and experience to develop and maintain ETL pipelines for ingestion from multiple sources.
· Cloud-Native SQL: Expertise in AWS or Azure PostgreSQL DB and Azure Functions.
· AWS- Hands-on experience with S3 (storage), SageMaker (workflows), and DynamoDB (NoSQL); Glue, Lambda).
· DevOps & CI/CD : Experience with DevOps for deploying code from Git (CI/CD implementation).
· SQL Knowledge: Strong knowledge of SQL (DDL/DML/Data Query optimization).
· Testing: Proven experience in writing robust unit and integration test cases.
· Collaborative Engineering: High comfort level with GitHub (branching, PRs, code reviews) and Jupyter Notebooks.
· Modeling: Strong foundation in statistical Modeling and machine learning using Google Meridian and scikit-learn.
Secondary Skills Required / Good To Have
· Media Science: Exposure to Marketing Mix Modeling (MMM), attribution modelling, or media effectiveness measurement.
· MLOps: Experience with MLflow for model versioning and tracking.
· Advanced Frameworks: Experience with Bayesian MMM (e.g., Google Meridian).
· DevOps Awareness: Knowledge of Docker and CI/CD workflows (GitHub code push/automation).
