Sr. Data Scientist / Data Scientist

Position Overview:

As a Data Scientist at SSL Wireless, you will contribute to a diverse range of AI and data-driven projects across various domains, leveraging your expertise in Natural Language Processing (NLP), Speech Recognition, Computer Vision, Time Series Data Analysis, and Data Analytics. You will lead a team of data scientists, manage multiple projects, and play a crucial role in shaping business decisions and delivering innovative solutions.

Key Responsibilities:

Natural Language Processing (NLP) & Speech Recognition:

• Develop and optimize solutions for automatic transcription and summarization of meeting minutes, ensuring accuracy and efficiency.
• Build conversational AI systems for customer support that integrate speech recognition and real-time voice response capabilities.

Time Series Data Analysis & Forecasting:

• Implement predictive models to forecast mobile recharge amounts for telecom users, integrating live data visualization dashboards to track and display usage trends.

Data Analytics & Performance Monitoring:

• Perform detailed analysis on employee performance and project engagement, providing insights into productivity through data-driven techniques.
• Develop HR analytics tools for monitoring employee activity, attendance trends, and time compensation, enabling efficient workforce management.

Optical Character Recognition (OCR) & Document Parsing:

• Design and deploy OCR-based solutions to automate the extraction of data from documents such as lab reports, bank statements, and invoices, improving operational efficiency.
• Develop algorithms to parse resumes in multiple formats, automating the candidate profiling process for recruitment platforms like LinkedIn.

Computer Vision & Recognition:

• Deliver real-time facial recognition solutions for multi-face detection in live video streams, verifying identities against official documents such as NID and passports.
• Create computer vision systems to classify product images and identify shop types and sizes, focusing on key sectors such as grocery, pharmaceuticals, and telecom.

Leadership & Business Engagement:

• Lead a team of 4-5 data scientists, assigning tasks based on each team member’s strengths and guiding them through project development and decision-making processes.
• Engage with external customers, company directors, and C-level executives to discuss data science use cases, project implementation, and real-world applications, playing a key role in business decision-making.

MLOps & End-to-End Project Delivery:

• Manage the entire project lifecycle, from database interaction and data processing to model training, API development, Dockerization, and deployment on remote servers.
• Ensure the smooth delivery of AI and data science solutions as a service to end customers.