Data Science Engineer I - US

Key Responsibilities:

• Assist in developing, training, and validating machine learning models for real-world applications (e.g., classification, prediction, and recommendation systems).
• Build and maintain data ingestion pipelines from structured and unstructured sources using Python and SQL-based tools
• Perform data cleaning, normalization, and feature engineering to prepare high-quality datasets for ML training and evaluation.
• Collaborate on ML projects such as outcome prediction systems, image classification models, and intelligent search interfaces.
• Contribute to building interactive applications by integrating ML models into frontend/backend systems (e.g., React, Django, REST APIs).
• Participate in MLOps workflows, including model versioning, basic deployment tasks, and experiment tracking.
• Document data flows, ML experiments, and application logic consistently.
• Attend Agile meetings and collaborate with peers through code reviews and sprint activities.

Required Qualifications:

• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
• Experience in machine learning, data engineering, or software development roles (internships or academic projects acceptable).
• Solid understanding of supervised learning, classification, and data preprocessing techniques.
• Experience with data engineering concepts, including SQL, PostgreSQL, and REST API integration
• Basic knowledge of data ingestion and transformation concepts.
• Proficiency in Python and common ML libraries (e.g., scikit-learn, pandas, NumPy, TensorFlow or PyTorch).
• Familiarity with full-stack or web-based ML applications (e.g., React, Django, or Android Studio projects).
• Familiarity with SQL and data wrangling tools.
• Experience with version control tools like Git.
• Strong problem-solving skills and attention to detail.
• Effective communication and documentation skills.
• Enthusiasm for learning new tools and growing within a collaborative team environment

Preferred Qualifications:

• Exposure to cloud platforms such as AWS, GCP, or Azure.
• Experience with pyton, Spark, Airflow, or data pipeline frameworks.
• Understanding of basic data architecture concepts (e.g., data lakes, warehouses).
• Participation in ML/DS projects, hackathons, or Kaggle competitions.

Sponsorship

• This role is not sponsorship eligible
• Candidates need to be legally allowed to work in the US for any employer