AI Data Scientist

The Rose
3 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Remote

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Computer Vision
Azure
Big Data
Cloud Computing
Data Cleansing
Data Transformation
Hadoop
Python
Machine Learning
Natural Language Processing
NumPy
Recommender Systems
Power BI
TensorFlow
Tableau
Unstructured Data
Data Processing
Google Cloud Platform
Feature Engineering
PyTorch
Large Language Models
Spark
Deep Learning
Generative AI
Pandas
Matplotlib
Scikit Learn
Performance Monitor
Data Management
Machine Learning Operations
Data Pipelines

Job description

  • Analyze structured and unstructured data to identify trends and patterns
  • Build, train, and evaluate machine learning and AI models
  • Develop predictive models, recommendation systems, and AI-driven solutions
  • Perform data preprocessing, feature engineering, and data transformation
  • Work with large datasets using statistical and analytical techniques
  • Implement and optimize deep learning models when required
  • Collaborate with data engineers to prepare data pipelines
  • Communicate insights and results to stakeholders through visualizations
  • Deploy models into production and monitor performance
  • Ensure data quality, integrity, and compliance with standards

Requirements

  • Strong knowledge of Python or R
  • Understanding of machine learning algorithms and AI concepts
  • Knowledge of statistics and probability
  • Experience with data analysis and visualization tools
  • Familiarity with data processing libraries (pandas, NumPy)
  • Understanding of data cleaning and feature engineering
  • Problem-solving and analytical thinking, * Experience with deep learning frameworks (TensorFlow, PyTorch)
  • Familiarity with Generative AI and LLMs
  • Knowledge of Natural Language Processing (NLP) or Computer Vision
  • Experience with big data tools (Spark, Hadoop)
  • Exposure to MLOps practices
  • Familiarity with cloud platforms like Amazon Web Services, Microsoft Azure, Google Cloud Platform
  • Understanding of model deployment and API integration, * Languages: Python, R
  • Libraries: pandas, NumPy, Scikit-learn
  • Frameworks: TensorFlow, PyTorch
  • Visualization: Power BI, Tableau, Matplotlib
  • Big Data: Spark, Hadoop
  • Platforms: AWS, Azure, Google Cloud Platform

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