Data Scientist

As a Data Scientist you will play a key part in designing, developing, and deploying innovative large language models to address real-world challenges and business needs. You will collaborate with researchers, data scientists, and software engineers to push the boundaries of AI technology and create next-generation applications for maximizing value for business.

key accountabilities

key result / decision areas (outcomes)

• Manage and deliver all assigned projects as per agreed time frame, allocated budget, resource and quality criteria.
• Managing project risks, including the development of contingency plans
• Responsible for Identification &  all the communication with the stakeholders.

Project plan adherence with a view on coordination of multiple projects/activities in the team.

Timely interaction with stakeholders and update the progress of activity, risks, completion status

Regular tracking of project risks and share with relevant stakeholders

Work with product, Technical and Customer Support on identifying problems in different areas where machine learning/statistics can help.

Present your findings to both technical and non-technical audiences.

Lead traversal technical discussion with Digital Technology and GDO team

Participate in the entire LLM development lifecycle, from problem definition and data preparation to model training, evaluation, and deployment.

Identify and own Use case coming from Different BU and lead it from doing POC till industrialization

Design and implement novel LLM architectures and training techniques, leveraging deep learning frameworks like TensorFlow and PyTorch.

Develop and maintain efficient pipelines for data preprocessing, training, and inference.

Collaborate with data scientists to curate, clean, and prepare high-quality training data for LLMs.

Develop, contribute and Lead technical framework for AI use case environment

Evaluate LLM performance using appropriate metrics and identify opportunities for improvement.

LLM performance evaluation and define metrices

Apply advanced machine learning algorithms, statistical methods and predictive modeling techniques on large and varied data sets that include application logfiles, other online application telemetry, structured and unstructured data sources

Building machine Learning model and application.
help and act as Technical support to Other DS within team.

Act as technical consultant for Data & AI  use case.

Deploy LLMs to production environments and monitor their performance for accuracy, fairness, and efficiency

Building machine Learning model and application.
help and act as Technical support to Other DS within team.

Act as technical consultant for Data & AI  use case.

Design and develop front-end interfaces for AI-powered applications using modern web technologies such as HTML, CSS, JavaScript, and frameworks like React or Angular.

Lead and Research and development initiative within Team and come up industry standard practice within team to follow in ongoing projects.

Stay up-to-date on the latest advancements in LLM research and identify opportunities to incorporate them into your work

Act as SPOC from data & AI team to leverage AI capabilities with Business team.

Build scalable back-end systems and APIs to support AI model integration and data processing using languages such as Python, Java, or Node.js.

Collaborate with researchers to explore new applications for LLMs in various domains.

Define Solution design proposal  for business

Manage the infrastructure needed for data scientists to run their experiments and deploy models. This includes setting up compute clusters with GPUs or TPUs for computationally intensive tasks, configuring cloud storage for datasets, and managing containerized environments for model deployment

Document your work clearly and concisely, including research papers, technical reports, and code documentation.