Role Overview:

Key Responsibilities:

• Problem Understanding
  • Collaborate with cross-functional teams to identify and understand business challenges.
  • Translate business problems into analytical tasks and define the problem scope.
• Data Exploration and Preparation
  • Collect, clean, and preprocess large datasets from multiple sources.
  • Perform exploratory data analysis (EDA) to extract meaningful insights.
• Model Development and Time-Series Analysis
  • Design, develop, and optimize AI/ML models to solve complex problems.
  • Apply advanced techniques to analyze and forecast time-series data.
  • Implement end-to-end machine learning pipelines, from data ingestion to deployment.
• Deployment and Scalability
  • Build and deploy machine learning models into production environments.
  • Ensure scalability, robustness, and efficiency of deployed models.
  • Implement MLOps practices for model lifecycle management.
• Communication
  • Present technical findings and actionable insights to non-technical stakeholders.
  • Create visualizations and reports to communicate results effectively.
• Continuous Improvement
  • Stay updated with the latest advancements in AI/ML technologies.
  • Experiment with new tools and techniques to enhance model performance

Experience

• 3-5 years of hands-on experience in AI/ML model development, deployment, and lifecycle management.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related fields.

Required Skills:

• Proficiency in programming languages like Python.
• Strong knowledge of machine learning algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and optimization techniques.
• Experience working with time-series data analysis and forecasting techniques.
• Expertise in deploying models using cloud platforms (AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes).
• Excellent communication and presentation skills to convey complex ideas clearly.