Suhail
Unnao, Lucknow Division, Uttar Pradesh, India
Experience: 2 years
Open to: Full-Time
Education: Masters
Availability: Within 30 Days
Skills: Docker, Git, Grafana, Jenkins, Kubernetes, Matplotlib, MongoDB, MySQL, NumPy, pandas, Postman, Python, Python3, PyTorch, R, Redis, scikit-learn, Tableau, TensorFlow
Previously worked at: Startups
Assessment Score: 100
- Advanced Data Science Expertise: Proficient in Python, R, and SQL, with extensive experience in machine learning algorithms, statistical modeling, and data visualization. Specialized in tools like TensorFlow, PyTorch, FlaskAPI, and Docker, combined with advanced domains such as NLP, computer vision, and time-series analysis.
- Educational Foundation in Data Science: Holds a Master of Science in Data Science from Delhi University and a Bachelor of Science in Computer Science from Aligarh Muslim University, providing a strong theoretical and practical foundation in computational and analytical skills.
- Professional Excellence as a Data Scientist: At ExactSpace Technologies, developed and maintained over 8,000 predictive models, enhanced model accuracy by up to 60% through innovative techniques, and reduced manual intervention in workflows by leveraging automation for retraining and benchmark distribution updates.
- Impactful Projects and Innovations: Successfully implemented a speech-to-text transcription system for the Indian Air Force, leveraging WhisperAI and HuggingFace, and developed an interactive object detection and fractal tree visualization system responding to real-time hand movements.
- Collaborative Deployment and Development: Facilitated the deployment of 7+ machine learning services across NLP and computer vision platforms, collaborating with cross-functional teams to streamline processes and increase the accessibility of predictive analytics through Flask-based APIs.
- Cutting-Edge Algorithm Development: Pioneered algorithms for feature optimization, outlier filtering, and data drift detection, achieving significant improvements in model performance, efficiency, and training time while aligning closely with domain-specific requirements.