Junior Data Scientist - AI Model Development

What role will you play in the team: As a Junior Data Scientist, you will contribute to the development and deployment of AI models. You will work closely with senior data scientists to analyze data, build models, and evaluate their performance, driving innovation and efficiency.

What you will do: You'll be involved in data preprocessing, feature engineering, model selection, and hyperparameter tuning. You will also participate in code reviews, documentation, and the maintenance of existing models to solve critical business problems.

Key Responsibility:

• Assist in the entire lifecycle of machine learning models, from data collection to deployment.
• Conduct exploratory data analysis to identify patterns, trends, and insights.
• Develop and implement data preprocessing and feature engineering techniques.
• Build, train, and evaluate machine learning models using Python and relevant libraries.
• Collaborate with senior data scientists to refine models and improve performance.

• Document code, algorithms, and model performance metrics.
• Stay up-to-date with the latest advancements in AI and machine learning.

Required Qualification and Skills:

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field.
• Proficiency in Python and experience with libraries like scikit-learn, TensorFlow, or PyTorch.
• Strong understanding of statistical modeling and machine learning algorithms.
• Experience with data visualization tools like Tableau or Power BI is a plus.
• Familiarity with database management systems like SQL or MongoDB.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.

Benefits Included:

• Competitive salary and benefits package.
• Opportunity to work on challenging and impactful projects.
• Mentorship and guidance from experienced data scientists.
• Exposure to cutting-edge technologies and tools.
• Professional development opportunities to enhance your skills and knowledge.
• Health insurance and retirement plans.
• Paid time off and flexible work arrangements.

A Day in the Life: You might start by reviewing model performance reports, then collaborate with the team on feature engineering tasks, followed by training a new model and documenting the results.