Data Scientist

KEY RESPONSIBILITIES

Active Learner with Enthusiasm for Problem-Solving
Continuously learn and apply new AI/ML techniques, including Generative AI, Agentic AI deep learning, and other emerging technologies. Demonstrate a passion for solving complex business challenges and experimenting with novel approaches to improve model performance.

Quick Prototyping & Experimentation
Develop rapid prototypes to test new AI/GenAI/ML approaches and solutions for business problems. Implement proof-of-concept models quickly, enabling fast experimentation and iteration to assess feasibility before scaling to full development.

Support AI Product Development & Iteration
Collaborate with lead/ senior data scientists and cross-functional teams to support the design, development, and deployment of AI-driven products. Contribute to the creation of prototypes and refine models based on business needs and feedback.

Model Development and Optimization
Design, develop, and test machine learning, deep learning, and generative AI models, focusing on performance, scalability, and business relevance. Continuously refine and optimize models based on real-world results and metrics.

Data Preparation & Collaboration
Assist in defining data requirements and support data engineers in building data pipelines. Clean and preprocess datasets, ensuring data quality and preparing it for use in machine learning applications. Collaborate closely with engineers to integrate models into production systems.

Research Support and Knowledge Sharing
Stay informed about the latest trends and research in AI/GenAI/ML and generative models, applying this knowledge to current projects. Actively participate in team discussions, contribute to the evaluation of new techniques, and share insights with peers to foster a collaborative learning environment.

ESSENTIAL SKILLS

·       Good knowledge of Python programming language, data engineering and data science ecosystem in Python.

·       Hands on experience in Generative AI, Agentic AI, LLM and Advance RAG techniques.

·       Hands on experience in developing and supporting Machine Learning solutions.

·       Hands on experience in developing Natural Language Processing (NLP) solutions

·       Hands on experience on SQL ecosystem

·       Hands on experience on Solr framework with Python

·       Experience of working on Linux and shell scripting

·       Basic statistical modelling knowledge

·       Strong Computer Science fundamentals are must. (Data structures, Algorithms, OS, Databases).

·       Good communication and organizational skills with significant attention to detail

·       Experience partnering with cross-functional teams of domain experts, engineers, data scientists, and production support teams.

·       Strong attention to detail with excellent problem-solving skills.

·       Desire and ability to thrive in a distributed technical environment while working on multiple projects simultaneously.

·       Passionate about latest technology trends with a strong desire for innovation.

·       Self-motivated, self-managing and able to work independently.

EDUCATION AND EXPERIENCE

·       Bachelor's degree (B.E/ B Tech. computer science, engineering, statistics/mathematics or a related field) from a four-year college or university, or equivalent, master's a plus.

·       Total at least 1 years of experience working on enterprise AI products

·       Experience in supporting software products or applications in the AI ML domain.

·       The above statements are intended to describe the general nature and level of work being performed by most people assigned to this job. They are not intended to be an exhaustive list of all duties and responsibilities and requirements.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.