Data Engineer Intern
We are looking for a highly motivated Data Engineer Intern with a passion for data-driven solutions and a knack for building scalable data processing systems. The ideal candidate will have a strong foundation in data analysis and developing NLP-based applications. You will be instrumental in creating semantic product search and basket analysis tools/frameworks for the retail industry.
Key Responsibilities
- Design and implement scalable data processing and analysis pipelines.
- Develop and integrate NLP/LLM models for semantic product search and basket analysis.
- Collaborate with data scientists and engineers to refine data for predictive modeling.
- Assist in the collection, cleansing, and transformation of large data sets.
- Evaluate and improve the performance of existing data systems and processes.
- Participate in the development of algorithms for personalized customer recommendations.
- Research emerging technologies and methodologies to enhance our data capabilities.
Qualifications
- Currently pursuing a degree in Computer Science, Data Science, Engineering, or a related field.
- Strong programming skills in Python, including experience with Pandas, NumPy, and Scikit-learn.
- Familiarity with NLP tools and libraries (e.g., NLTK, spaCy, or Transformers).
- Experience with data processing and analysis tools (e.g., SQL, Apache Spark).
- Understanding of machine learning concepts and algorithms.
- Excellent problem-solving and analytical skills.
- Ability to work collaboratively in a team environment.
- Strong communication skills, both verbal and written.
Preferred Qualifications
- Prior experience or projects involving NLP or machine learning.
- Knowledge of cloud computing services (e.g., AWS, Google Cloud Platform).
- Familiarity with version control systems, preferably Git.
What We Offer
- Competitive stipend based on your skillset
- Hands-on experience with real-world data engineering and NLP projects.
- Databricks training.
- Azure/AWS/GCP data infrastructure access
- Mentorship from experienced professionals in the field.
- A collaborative, innovative, and inclusive work environment.
- Opportunities for professional development and networking.