Senior Data Science Engineer at Scrabble Infotech Solutions | Torre
Senior Data Science Engineer
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Scrabble Infotech Solutions

Senior Data Science Engineer

Scrabble Infotech Solutions
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You'll drive data-driven decisions and business strategy through end-to-end ML development and insights.
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Full-time

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Shared by
Diana Montoya
 16 days ago

Requirements and responsibilities

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 RequiredPython 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.
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