Senior Quantitative Researcher

1 day ago

Delhi, Delhi, India beBeeDataScience Full time ₹ 30,00,000 - ₹ 40,00,000
Job Title:

Data Science Manager

Key Skills and Qualifications:
  • A graduate degree in a quantitative field, such as computer science, statistics, applied mathematics, machine learning, or a related discipline is required.
  • Strong programming skills in Python with a working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc.
  • Experience with Large Language Models (Llama, T5, Falcon, Langchain, or similar)
  • Understanding of the evolution history of NLP, training data creation, training set-up and fine-tuning
  • Ability to interpret research papers and architecture diagrams of Language Models
  • Comfortable with LORA, RAG, Instruct fine-tuning, Quantization, etc.

Benefits:

  • Opportunity to lead a team of Data Engineers, Analysts, and Data Scientists
  • Chance to connect with internal/external stakeholders to understand business requirements
  • Freedom to create project plans and sprints for milestones/deliverables
  • Authority to spin VM, create and optimize clusters for Data Science workflows
  • Ability to create data pipelines to ingest data effectively
  • Responsibility to assure data quality with proactive checks and resolve gaps
  • Opportunity to carry out EDA, Feature Engineering & Define performance metrics prior to running relevant ML/DL algorithms
  • Chance to research similar solutions before building ML models
  • Authority to create optimized data models to query relevant data efficiently
  • Ability to run relevant ML/DL algorithms for business goal seek
  • Responsibility to optimize and validate these ML/DL models to scale
  • Opportunity to create light applications, simulators, and scenario builders to help business consume end outputs
  • Authority to test codes pre-production for possible bugs and resolve these bugs proactively
  • Chance to integrate and operationalize models in client ecosystem
  • Responsibility to document project artifacts and log failures and exceptions
  • Opportunity to measure, articulate impact of DS projects on business metrics and fine-tune workflow based on feedbacks

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