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Data Science Innovator
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We are seeking a highly skilled professional to spearhead our team of data engineers, analysts, and scientists.
The ideal candidate will possess a graduate degree in a quantitative field such as computer science, statistics, or applied mathematics.
Strong programming skills in Python, with a working knowledge of popular numerical, data analysis, and AI frameworks like NumPy, Pandas, and Scikit-learn, are required.
Experience with large language models (LLMs) such as Llama, T5, Falcon, and Langchain is also essential.
The candidate should be well-versed in the evolution history of natural language processing (NLP), from traditional language models to modern large language models. They must be comfortable interpreting research papers and architecture diagrams of language models.
Key Responsibilities:
- Lead a team of data engineers, analysts, and scientists to execute various activities
- Collaborate with internal/external stakeholders to understand business requirements
- Coordinate with relevant stakeholders to gather data artifacts, anecdotes, and hypotheses
- Create project plans and sprints for milestones/deliverables
- Spin virtual machines, create and optimize clusters for data science workflows
- Develop data pipelines to ingest data effectively
- Ensure data quality with proactive checks and resolve gaps
- Conduct exploratory data analysis, feature engineering, and define performance metrics prior to running relevant machine learning/distributed learning algorithms
- Research existing solutions before building machine learning models
- Create optimized data models to query relevant data efficiently
- Run relevant machine learning/distributed learning algorithms for business goals
- Optimize and validate these models to scale
- Develop light applications, simulators, and scenario builders to help business consume end outputs
- Create test cases and test codes pre-production for possible bugs and resolve these bugs proactively
- Integrate and operationalize models in client ecosystems
- Document project artifacts and log failures and exceptions
- Measure and articulate impact of DS projects on business metrics and fine-tune workflow based on feedbacks
