Data Science Engineer

2 days ago

tamil nadu, India Populus Empowerment Network Full time

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)

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