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Job Description

About the job Data Scientist (AI Forecasting)

Aumet is a leading healthcare technology company dedicated to revolutionizing the way medical supplies are sourced and distributed globally. Our platform connects healthcare providers with a vast network of suppliers, streamlining the procurement process and ensuring efficient access to essential medical products.


Job Overview:
The Data Scientist leverages AI and machine learning techniques to develop predictive models for forecasting prescription and dispensing patterns. This role analyzes large datasets, optimizes algorithms, and collaborates with other teams to integrate AI-driven insights into the platform.


Responsibilities:


  • Develop and optimize AI algorithms for forecasting prescription and dispensing patterns.
  • Collaborate with the backend and data engineering teams to integrate AI models into the platform.
  • Perform data analysis on large datasets to derive insights and improve model accuracy.
  • Use machine learning techniques to predict demand and supply patterns in healthcare and pharmaceutical fields.
  • Continuously improve AI models based on feedback, performance metrics, and new data.
  • Create and maintain documentation on model processes, assumptions, and results for non-technical stakeholders.
  • Stay up-to-date with the latest trends and advancements in AI, particularly in forecasting and predictive analytics.

Requirements:


  • A minimum of 4 years of experience
  • Proficient in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, Scikit-learn.
  • Solid understanding of machine learning algorithms, including regression, classification, clustering, and time series forecasting.
  • Strong data analysis skills with proficiency in SQL and familiarity with NoSQL databases (e.g., MongoDB).
  • Experience in model deployment and integration with backend systems (e.g., Flask, FastAPI).
  • Knowledge of data wrangling and feature engineering.
  • Familiarity with real-time data analysis tools and techniques.
  • Experience in analyzing and processing large datasets.
  • Strong problem-solving skills and ability to work with unstructured data.
  • Excellent communication skills to explain technical concepts to non-technical stakeholders.


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