Data Scientist

AI/ML Model Development: Utilize available models to design, train, and deploy machine learning models. Leverage Cloud / On premise / Hybrid environments platforms like Amazon SageMaker and Azure Machine Learning for end-to-end machine learning lifecycle management.

Data Architecture Design: Architect and manage robust scalable data infrastructures supporting AI/ML project requirements including data ingestion, storage, and processing workflows.

Data Processing: Conduct data cleaning, preprocessing, and analysis, preparing datasets for model training for effective data handling and insights generation.

Automation Services Integration: Automate data pipelines and integrate AI/ML models into production systems. Enhance business processes and decision-making capabilities through cloud-based AI solutions.

Performance Monitoring: Monitor the performance of AI models and the efficiency of data pipelines across cloud platforms, adjusting resources and configurations to ensure optimal operation.

Cross-Functional Collaboration: Collaborate with enterprise architects, data scientists, and business stakeholders to identify and exploit AI-driven automation opportunities on AWS, Azure.

Preferred Skills/Experience:

Strong analytic skills with the ability to extract, collect, organize, analyze and interpret trends or patterns in complex data sets

Experience in analytics, advanced analytics/statistics, predictive modeling, machine learning, data visualization

Understanding of machine learning techniques and algorithms

Experience in python/R/SAS/SQL for data extraction, data mining, and predictive analytics

Demonstrated project management skills

Effective interpersonal, verbal and written communication skills

Qualifications:

Bachelors degree in computer science, Information Technology, or a related field.

Certifications in relevant fields is preferred, but it is not mandatory.