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    Data Scientist - Bangalore, India - Scaling Theory

    Scaling Theory
    Scaling Theory Bangalore, India

    2 days ago

    Default job background
    permanent Technology / Internet
    Description

    Job Description :

    The primary job description of a Data Science Engineer involves using data analysis, statistical methods, and machine learning to extract insights, build predictive models, and solve complex business problems. You will also be responsible for the design and maintenance of data pipelines, collaborating with cross-functional teams, and deploying proprietary models.

    Responsibilities :

    • Utilize advanced statistical techniques and machine learning algorithms to analyze large datasets.
    • Develop predictive models, clustering methods, and recommendation systems to extract valuable insights.
    • Design and maintain data pipelines for efficient data ingestion, transformation, and storage.
    • Ensure data quality and reliability, implementing monitoring and error handling mechanisms.
    • Collaborate with data engineers, software developers, and business stakeholders to understand data requirements.
    • Translate business problems into data-driven solutions, contributing to decision-making processes.
    • Deploy machine learning models into production environments and monitor their performance.
    • Continuously optimize models for accuracy and efficiency, considering scalability and real-time requirements.
    • Document data science processes, methodologies, and findings.
    • Prepare clear and concise reports and presentations to communicate insights to non-technical stakeholders.
    Requirements :
    • Machine Learning Expertise : Proficiency in machine learning algorithms, model training, and evaluation.
    • AWS SageMaker : In-depth knowledge of SageMaker services, including model development, deployment, and management.
    • Programming : Strong programming skills, especially in Python, for developing and customizing machine learning models.
    • Data Preprocessing : Ability to preprocess and clean data efficiently for model training.
    • AWS Services : Familiarity with relevant AWS services like S3 for data storage, Lambda for serverless computing, and CloudWatch for monitoring.
    • Containerization : Understanding of Docker containers for packaging and deploying machine learning models.
    • Data Visualization : Proficiency in data visualization tools and libraries like Matplotlib or Seaborn.
    • DevOps : Knowledge of DevOps practices for automating model deployment and management pipelines.
    • Security : Awareness of AWS security best practices to ensure data and model security.
    • Collaboration: Effective communication and collaboration skills to work with cross-functional teams
    )
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