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)