Junior Data Science Engineer - Recommendation Systems

What role you will play in team:

As a Junior Data Science Engineer, you'll contribute to our team's efforts in developing and maintaining recommendation systems. Your role involves data analysis, model implementation, and collaboration with senior engineers to improve the accuracy and efficiency of our recommendations.

What you will do:

You will work on collecting and preprocessing data, experimenting with different algorithms, implementing machine learning models for personalized recommendations, and evaluating their performance using appropriate metrics. You will also contribute to the creation of scalable solutions.

Key responsibility:

• Assist in the development and implementation of recommendation algorithms.
• Collect, clean, and preprocess large datasets for model training.
• Evaluate model performance using metrics like precision, recall, and NDCG.
• Collaborate with senior engineers to optimize model performance and scalability.
• Write clean and well-documented code.
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  Stay up-to-date with the latest advancements in recommendation systems and machine learning.
• Contribute to the development of data pipelines.
• Participate in code reviews and knowledge sharing.

Required Qualification and Skills:

• Bachelor's degree in Computer Science, Statistics, or a related field.
• Proficiency in Python and experience with machine learning libraries such as scikit-learn and TensorFlow or PyTorch.
• Familiarity with data visualization tools like Tableau or Power BI.
• Experience with SQL and database management.
• Understanding of recommendation system algorithms (e.g., collaborative filtering, content-based filtering).
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork skills.
• Basic understanding of Big Data technologies like Hadoop or Spark is a plus.

Benefits Included:

• Competitive salary and benefits package.
• Opportunities for professional development and training.
• Health insurance and paid time off.
• Exposure to cutting-edge technologies and challenging projects.
• Supportive and collaborative work environment.
• Flexible work arrangements (Hybrid).
• Employee assistance programs.
• Performance-based bonuses.

A Day in the Life:

You'll start your day by checking in with the team on project progress. You'll spend time analyzing data, experimenting with different algorithms, and implementing new features for the recommendation system. You will also collaborate with senior engineers to troubleshoot issues and optimize model performance.