Principal Data

Responsibilities:

1. Lead strategic data science initiatives aligned with business objectives, ensuring AI, machine learning, and generative AI solutions deliver measurable impact.
2. Design and implement end-to-end AI solutions, from problem definition and data acquisition to model development, deployment, and post-production monitoring.
3. Define goals and deliverables for data analytics, big data, and automation projects, establishing success criteria and key performance metrics to evaluate solution effectiveness.
4. Select and apply appropriate modeling techniques to solve complex problems involving large-scale structured and unstructured datasets. Implement continuous learning and iterative model enhancements.
5. Develop advanced solutions using cutting-edge techniques such as AutoML and state-of-the-art machine learning algorithms, leveraging the latest trends in AI to train models on new datasets.
6. Guide teams in feature engineering, experimentation, and advanced modeling approaches for complex challenges involving streaming data, raw text, and other unstructured sources.
7. Lead internal excellence initiatives and client-facing projects, reviewing junior team members' deliverables and providing mentorship and technical guidance.
8. Oversee the full lifecycle of machine learning solutions, from design and development to deployment, including Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) applications.
9. Drive innovation across model development pipelines, ensuring scalability, reliability, and seamless integration into enterprise systems.
10. Mentor and manage a team of data scientists and engineers, fostering a collaborative, high-performance culture.

Requirements:

1. Advanced degree in Computer Science, Engineering, Mathematics, or a related quantitative discipline, with a minimum of 5 years of industry experience.
2. Exceptional communication, writing, and analytical thinking skills.
3. Proficient in Python and experienced with at least one major cloud platform (AWS, Azure, or GCP).
4. Proven ability to collaborate across cross-functional teams, including analytics, development, product management, and operations, and effectively communicate technical concepts to both technical and non-technical audiences.
5. Demonstrated success in leading agile data science teams and deploying scalable machine learning solutions, with a passion for mentoring early-career data scientists.
6. Strong foundation in machine learning algorithms (e.g., regression, classification, clustering), statistical modeling, and deep learning frameworks (e.g., TensorFlow, PyTorch, Keras, MxNet, H2O).
7. Expertise in designing and executing scientific experiments, with advanced skills in statistical analysis and hypothesis testing.
8. Solid knowledge of Operations Research, AI algorithms (e.g., constraint satisfaction problems, search), system architecture, and big data technologies (e.g., MapReduce).
9. Experience developing and integrating APIs for AI model consumption within enterprise applications.
10. Hands-on experience with training, fine-tuning, and deploying generative AI models (e.g., GANs, VAEs, transformers).
11. Proficiency in architecting and optimizing Agentic AI systems and RAG-based solutions.
12. Familiarity with vector databases and retrieval-based AI models for conversational and information retrieval applications.
13. Deep understanding of MLOps best practices, including model versioning, monitoring, CI/CD pipelines, automated retraining, and maintaining long-term model performance.

Benefits:

1. Provident Fund
2. Performance based Bonuses
3. Annual Increments
4. Health Insurance
5. Paid Leaves
6. Certification Reimbursement
7. Gym Membership