Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.indeed.com

Tech stack

Business Analytics Applications Data Mining Relational Databases Design of User Interfaces Python (Programming Language) Machine Learning Performance Tuning SAS (Software) Large Language Models Deep Learning

Job description

• Participate in data extraction, transformation, and preparation.
• Resolve common data issues and ensure quality for model development.
• Participate in developing models using statistical or machine learning techniques and collaborate with technology teams to operationalize them into analytics tools or scripts.
• Participate in model testing and validation, selecting the best-performing algorithms based on statistical and business metrics.
• Participate in the development of advanced analytics and machine learning or deep learning models including LLMs using predefined processes and tools like SAS and R/ Python.
• Participate in defining analytics problems; execute visualization, analysis, and predictive modeling with senior support.
• Identify data sources and extract from RDBMS and develop UI/UX for client usage.
• Participate in model performance, while making minor adjustments, and escalate risks or compliance concerns and generate reports on deviations or schedule slippages.
• Proactively participate in detailed documentation of model development, testing, and deployment activities for reproducibility.
• Work closely with business and technology teams to translate requirements into actionable models, while communicating results effectively.
• Apply predefined quality measurement frameworks, if any, to individual project tasks.
• Participate in deploying analytics tools in test and production environment, while ensuring they meet operational requirements.

Requirements

• Strong analytical and problem-solving mindset with hands-on model development skills.
• Ability to translate business needs into actionable analytics solutions.
• Focus on data quality, validation and performance optimization.
• Effective collaboration with business and technology stakeholders.
• Commitment to continuous learning, knowledge sharing, fostering team development.

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