Chief Data Scientist

4 days ago

Bellary, Karnataka, India beBeeDataScience Full time US$ 1,50,000 - US$ 2,00,000

Job Title: Data Science Leadership Position

This position plays a crucial role in driving the company's data science strategy and overseeing the development of large language models.

Job Description

We are seeking a highly skilled Data Science Manager to lead our team of data engineers, analysts, and scientists. The successful candidate will be responsible for collaborating with internal/external stakeholders to understand business requirements and gathering relevant data artifacts, anecdotes, and hypotheses.

Required Skills and Qualifications
  • A graduate degree in a quantitative field is required.
  • Good programming skills in Python with strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, and Scikit-learn.
  • Experience with large language models (LLMs), including Llama, T5, Falcon, and Langchain, or similar frameworks like Langchain.
  • Familiarity with the evolution of natural language processing (NLP), from traditional language models to modern large language models, training data creation, setup, and fine-tuning.
  • Ability to interpret research papers and architecture diagrams of language models.
  • Predictive modeling experience in Python using time series, multivariable, and causal methods.
  • Experience applying various machine learning techniques and understanding key parameters affecting their performance.

Benefits

The ideal candidate will have excellent verbal and written communication skills and be comfortable working in a dynamic, fast-paced, innovative environment with multiple ongoing projects.

Others

Key responsibilities include creating project plans and sprints for milestones/deliverables, setting up clusters and optimizing them for data science workflows, designing and optimizing data pipelines for effective data ingestion, ensuring data quality through proactive checks and resolving gaps, conducting exploratory data analysis, feature engineering, and defining performance metrics before running ML/DL algorithms, researching existing solutions before building ML models, creating optimized data models for efficient querying, running ML/DL algorithms for business goal attainment, optimizing and validating these models for scalability, developing light applications, simulators, and scenario builders for business consumption, creating test cases and testing codes pre-production for possible bugs and resolving them proactively, integrating and operationalizing models in client ecosystems, documenting project artifacts and logging failures and exceptions, measuring and articulating the impact of DS projects on business metrics and finetuning workflows based on feedback.

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