Data Scientist

Job Description

Data Scientist with Python Gen AI & AWS Cloud 
Python, Genai, AWS, NLP, AI/ML, LLM
Architect and implement Al solutions utilizing cutting-edge technologies like LLM, Lang chain, and Machine Learning.   
AIML solution development in Azure using Python
Ability to build and finetune the model to improve the performance
Create own technology if off-the-shelf technology is not solving the problem. E.g. changes to traditional RAG approaches, finetune LLM, create architectures.
Use experience and advise leadership and team of data scientists’ best approaches, architectures for complex ML use cases.
Lead from the front, responsible for coding, designing, and ensuring best practices & frameworks are adhered by the team.
Create end to end AI systems with responsible AI principles
Develop data pipelines using SQL to extract and transform data from Snowflake for Al model training and inference.
Possess expertise in Natural Language Processing (NLP) & Genai to integrate text-based data sources into the Al architecture.
Collaborate with data scientists and engineers to ensure seamless integration of Al components into existing systems.
Responsible for continuous communication about the team progress to key stakeholder 
 

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Do

1. Demand generation through support in Solution development

a. Support Go-To-Market strategy

i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales

b. Collaborate with different colleges and institutes for research initiatives and provide data science courses

2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions

a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions

b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options

3. Team Management

a. Talent Management

i. Support on boarding and training to enhance capability & effectiveness

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Deliver 

No.	Performance Parameter	Measure
1.	Demand generation	# PoC supported
2.	Revenue generation through delivery	Timeliness, customer success stories, customer use cases
3.	Capability Building & Team Management	# Skills acquired

 

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