Leading Data Science Expert

4 days ago

Rajahmundry, Andhra Pradesh, India beBeeDataScience Full time ₹ 20,00,000 - ₹ 25,00,000
Data Science Leadership Opportunity

We are seeking a seasoned Data Science Manager to lead our team of experts in developing innovative data-driven solutions.

  • Strong expertise in programming languages like Python, with experience in frameworks such as NumPy, Pandas, and Scikit-learn.
  • Familiarity with Large Language Models (LLMs), including Llama, T5, Falcon, and Langchain, as well as training data creation, setup, and fine-tuning.
  • Understanding of the evolution of Natural Language Processing (NLP) from traditional language models to modern large language models.
  • Ability to interpret research papers and architecture diagrams of language models.
  • Comfortable with techniques like LORA, RAG, Instruct fine-tuning, Quantization, and predictive modeling in Python.

  • Experience applying various machine learning techniques and understanding key parameters affecting performance.
  • Experience building systems capturing and utilizing large datasets to quantify performance via metrics or KPIs.
  • Excellent verbal and written communication skills.
  • Ability to work in a dynamic, fast-paced, innovative environment with multiple ongoing projects.

Key Responsibilities

The Data Science Manager will:

  • Collaborate with internal and external stakeholders to understand business requirements.
  • Coordinate with relevant teams to gather data artifacts, anecdotes, and hypotheses.
  • Create project plans and sprints for milestones and deliverables.
  • Design and implement data pipelines to effectively ingest data.
  • Assure data quality through proactive checks and resolve gaps.
  • Carry out exploratory data analysis, feature engineering, and define performance metrics prior to running machine learning algorithms.
  • Research existing solutions before building machine learning models.
  • Create optimized data models to query relevant data efficiently.
  • Run machine learning algorithms for business goals and optimize and validate models to scale.
  • Document project artifacts and log failures and exceptions.
  • Measure and articulate the impact of data science projects on business metrics and fine-tune workflows based on feedback.

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