Data Science Engineer

Key Responsibilities

• Develop and maintain backend systems for data processing and analytics
• Implement and optimize machine learning models for OCR and NLP applications
• Leverage Python and related frameworks to process large datasets efficiently
• Work with data engineering teams to integrate data pipelines and data sources into backend systems
• Design, implement, and deploy robust, scalable, and secure backend services
• Apply Data Science concepts to analyze, interpret, and leverage data for actionable insights
• Collaborate with front-end developers to integrate user-facing elements with server-side logic
• Stay up-to-date with the latest trends in data science, machine learning, OCR, and NLP technologies
• Troubleshoot and resolve issues in data models, pipelines, and production systems
• Mentor and guide junior developers and contribute to team knowledge sharing

Required Skills and Qualifications

• 4+years of experience as a Data Scientist or Backend Developer
• Proficiency in Python and relevant libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch)
• Strong knowledge of Data Science concepts, statistical analysis, and machine learning algorithms
• Experience with Optical Character Recognition (OCR) techniques and tools (e.g., Tesseract, OpenCV)
• Strong understanding and experience in Natural Language Processing (NLP) concepts (e.g., Named Entity Recognition, Sentiment Analysis, Text Classification)
• Experience with backend frameworks and technologies (e.g., Django, Flask, FastAPI)
• Familiarity with database systems (SQL, NoSQL) and cloud platforms (AWS, Azure, GCP)
• Solid experience with version control (e.g., Git) and agile development processes
• Excellent problem-solving and analytical skills
• Strong communication skills and the ability to work effectively in a collaborative team environment

Preferred Qualifications

Immediate Joiners are preferred.

• Master’s degree or higher in Computer Science, Data Science, or a related field
• Experience with deploying machine learning models in production
• Familiarity with containerization technologies (e.g., Docker, Kubernetes)
• Experience with big data tools and technologies (e.g., Hadoop, Spark)