Chief GenAI Data Science Strategist

3 days ago

Bikaner, Rajasthan, India beBeeDataScientist Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Lead Data Scientist for GenAI Development

This position involves leading a team of data engineers, analysts, and scientists in carrying out various activities related to data science workflows.

  • Connect with internal/external stakeholders to understand business requirements
  • Coordinate with relevant parties to gather all necessary data artifacts, anecdotes, and hypotheses
  • Create project plans and sprints for milestones/deliverables
  • Spin virtual machines, create, and optimize clusters for data science workflows
  • Create data pipelines to ingest data effectively
  • Assure the quality of data through proactive checks and resolve gaps
  • Carry out exploratory data analysis, feature engineering, and define performance metrics prior to running relevant machine learning/deep learning algorithms
  • Research whether similar solutions have been developed before building machine learning models
  • Create optimized data models to query relevant data efficiently
  • Run relevant machine learning/deep learning algorithms for business goal-seeking
  • Optimize and validate these machine learning/deep learning models to scale
  • Develop light applications, simulators, and scenario builders to help businesses consume end outputs
  • Test codes pre-production and resolve bugs proactively
  • Integrate and operationalize models in client ecosystems
  • Document project artifacts and log failures/exceptions
  • Measure, articulate impact of DS projects on business metrics and fine-tune workflows based on feedback

The ideal candidate will possess strong programming skills in Python, along with knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc. They will also be familiar with language models, including their evolution history, training data creation, setup, and fine-tuning.

The candidate should be comfortable interpreting research papers and architecture diagrams of language models, as well as being aware of key parameters affecting machine learning technique performance. Experience in predictive modeling, time series/multivariable/causal, and applying various machine learning techniques is essential.

A strong understanding of capturing and utilizing large datasets to quantify performance via metrics/KPIs is required. Excellent verbal and written communication skills are necessary for effective collaboration within a dynamic, fast-paced, innovative environment.

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