Data Scientist

As a Data Scientist III, you will lead the development and deployment of advanced data science solutions to address complex business challenges. You will work closely with stakeholders to understand requirements, guide the team, and ensure alignment with strategic objectives. This role demands deep expertise in machine learning, deep learning, GenAI, and MLOps, along with a strong ability to mentor junior team members and drive projects to successful completion. You will also identify opportunities for automation and process optimization to enhance operational efficiency.

Core Responsibilities

• Lead the design, development, and deployment of scalable ML/DL/GenAI solutions across diverse business domains.
• Translate business requirements into technical solutions, ensuring alignment with organizational goals.
• Drive automation of repeatable tasks and workflows to improve efficiency and reduce manual effort.
• Ensure high-quality deliverables through rigorous testing, validation, and implementation of quality control measures.
• Collaborate with cross-functional teams including data engineering, product, and business stakeholders.
• Communicate complex analytical concepts and insights clearly to both technical and non-technical audiences.
• Mentor and guide junior data scientists, fostering a culture of continuous learning and innovation.

Technical Expertise

• 5 -10 years of hands-on experience in Data Science, with proven success in applying ML, DL, NLP, and CV techniques to solve real-world problems.
• Strong proficiency in Python or R, with the ability to write modular, scalable, and production-ready code.
• Deep understanding of ML/DL algorithms, statistical modeling, and foundational concepts in NLP and computer vision.
• Experience with GenAI and LLMs, including applications such as summarization, content generation, and semantic analysis.
• Solid knowledge of SQL for data manipulation and analysis.
• Working knowledge of optimization techniques and their application in model tuning and performance enhancement.
• Hands-on experience with MLOps tools and practices for model lifecycle management, CI/CD, and monitoring.

Preferred Skills

• Experience with cloud platforms (e.g. GCP) and containerization tools (Docker, Kubernetes).
• Familiarity with model explainability frameworks and responsible AI practices.
• Exposure to data governance, privacy, and compliance standards.
• Ability to lead cross-functional teams and manage multiple projects simultaneously.