Overview

The Junior Data Scientist will contribute to AI model development, machine learning initiatives, and data automation projects. They will assist in building predictive analytics models, developing AI-powered solutions, and supporting process automation efforts to enhance business decision-making. Additionally, they will play a role in enhancing AI solutions to be market-ready and scalable for external clients. The role is designed for an individual with at least two years of experience working with data and AI technologies, focusing on practical applications in predictive analytics, NLP, and anomaly detection.

Key Responsibilities

• Develop and deploy machine learning models for predictive analytics, forecasting, and automation.
• Assist in designing AI-powered solutions as part of the company’s product offerings.
• Implement AI-driven process automation to improve operational efficiency.
• Enhance AI solutions to be commercially viable and scalable for external clients.
• Work with Natural Language Processing (NLP) techniques for chatbot development, sentiment analysis, and text-based AI applications.
• Support AI-powered decision-making processes by building automated recommendation systems.
• Analyze large datasets to extract insights and improve business performance.
• Collaborate with IT, Finance, and other departments to integrate AI models into business processes.
• Generate AI model performance reports, predictive analytics reports, and business automation impact analysis.

Required Qualifications & Experience

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Minimum of 2 years of experience working with data science, machine learning, or AI technologies.
• Proficiency in Python, TensorFlow, SQL, R, and Jupyter Notebooks.
• Experience with notable AI/ML frameworks.
• Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for AI model deployment is a plus.
• Ability to translate business problems into data science solutions.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.

Key Performance Indicators (KPIs)

• Accuracy and efficiency of developed AI models.
• Improvement in business automation and reduction in manual efforts.
• Impact of predictive analytics on decision-making processes.
• Quality and relevance of AI model performance reports.
• Success of AI-powered product offerings in meeting client needs.
• Growth and adoption of AI solutions by external clients.

Additional Skills (Not mandatory)

• Experience with deep learning techniques.
• Knowledge of Machine Learning Operations and AI model deployment strategies.
• Exposure to data visualization tools like Tableau or Power BI.
• Hands-on experience with API development and AI integration into business applications.