Full-Time Machine Learning Specialist
Budget: ₹1,250 – ₹2,500 INR
Duration: 1 year full-time
Working Hours: start at 6:30 AM India Time
Start date: ASAP
8+ yrs experience
Scope:
Model Development: Design, develop, and deploy machine learning models for various projects, focusing on improving predictive accuracy and business outcomes.
Data Analysis: Utilize advanced data analysis techniques to extract insights and inform decision-making. Conduct exploratory data analysis (EDA) to identify trends and patterns.
Feature Engineering: Build and maintain robust features and data pipelines showing a deep understanding of data manipulation and transformation techniques.
Collaboration: Work closely with cross-functional teams, including data engineers, product managers, and stakeholders, to understand data requirements and project goals.
Code Development: Write efficient, reusable code in Python for both backend development and data analysis, ensuring code quality through best practices and comprehensive testing.
Framework Implementation: Utilize frameworks like TensorFlow, PyTorch, and Scikit-learn for building machine learning applications and models.
Performance Monitoring: Monitor model performance, handle model updates, and apply techniques for model optimization and troubleshooting.
Documentation: Create comprehensive documentation for development processes, model architectures, and methodologies.
Mentorship: Provide guidance and support to junior developers and data scientists, fostering a collaborative learning environment.
Working Hours: start at 6:30 AM India Time
Start date: ASAP
8+ yrs experience
Scope:
Model Development: Design, develop, and deploy machine learning models for various projects, focusing on improving predictive accuracy and business outcomes.
Data Analysis: Utilize advanced data analysis techniques to extract insights and inform decision-making. Conduct exploratory data analysis (EDA) to identify trends and patterns.
Feature Engineering: Build and maintain robust features and data pipelines showing a deep understanding of data manipulation and transformation techniques.
Collaboration: Work closely with cross-functional teams, including data engineers, product managers, and stakeholders, to understand data requirements and project goals.
Code Development: Write efficient, reusable code in Python for both backend development and data analysis, ensuring code quality through best practices and comprehensive testing.
Framework Implementation: Utilize frameworks like TensorFlow, PyTorch, and Scikit-learn for building machine learning applications and models.
Performance Monitoring: Monitor model performance, handle model updates, and apply techniques for model optimization and troubleshooting.
Documentation: Create comprehensive documentation for development processes, model architectures, and methodologies.
Mentorship: Provide guidance and support to junior developers and data scientists, fostering a collaborative learning environment.