Data Scientist

Join to apply for the Data Scientist role at Dicetek LLC

Join to apply for the Data Scientist role at Dicetek LLC

• Data Cleaning and Preprocessing:
  Perform data cleaning, transformation, and preprocessing to ensure high-quality datasets for analysis and modelling.
• Machine Learning Algorithms:
  Develop and implement machine learning models using algorithms such as Random Forest, XGBoost, and others to solve complex problems.
• Forecasting:
  Utilize statistical and machine learning techniques for time series forecasting and predictive analytics.
• Python Programming:
  Leverage Python packages such as scikit-learn, pandas, and others for data analysis, modelling, and visualization.
• Artificial Intelligence:
  Apply AI techniques in computer vision, natural language processing (NLP), and generative AI using models like transformers and BERT. Incorporate prompt engineering and retrieval-augmented generation (RAG) applications.
• Cloud Computing:
  Deploy and manage machine learning and AI models on cloud platforms such as Azure or AWS, utilizing cognitive services.
• Database Management:
  Work with SQL and No
  
  SQL databases, including Amazon Redshift and vector stores/DBs, to store, retrieve, and manage data efficiently.
• Model Development and Deployment:
  Develop, test, and deploy machine learning and AI models, ensuring scalability and performance. Implement REST APIs using SOAP, Flask, Swagger, and Postman.
• Data Visualization:
  Create insightful visualizations and dashboards using tools like Power BI or Tableau.
• MLOps:
  Design and implement MLOps pipelines for continuous integration, continuous deployment (CI/CD), and monitoring of machine learning models. Ensure seamless deployment of models on cloud platforms and maintain robust pipelines for model updates and management.
• Research and Innovation:
  Stay updated with the latest research and advancements in data science, AI, and machine learning to continuously improve solutions.
• Certifications:
  
  Maintain relevant certifications to demonstrate expertise and commitment to professional development.

• Data Cleaning and Preprocessing:
  Perform data cleaning, transformation, and preprocessing to ensure high-quality datasets for analysis and modelling.
• Machine Learning Algorithms:
  Develop and implement machine learning models using algorithms such as Random Forest, XGBoost, and others to solve complex problems.
• Forecasting:
  Utilize statistical and machine learning techniques for time series forecasting and predictive analytics.
• Python Programming:
  Leverage Python packages such as scikit-learn, pandas, and others for data analysis, modelling, and visualization.
• Artificial Intelligence:
  Apply AI techniques in computer vision, natural language processing (NLP), and generative AI using models like transformers and BERT. Incorporate prompt engineering and retrieval-augmented generation (RAG) applications.
• Cloud Computing:
  Deploy and manage machine learning and AI models on cloud platforms such as Azure or AWS, utilizing cognitive services.
• Database Management:
  Work with SQL and No
  
  SQL databases, including Amazon Redshift and vector stores/DBs, to store, retrieve, and manage data efficiently.
• Model Development and Deployment:
  Develop, test, and deploy machine learning and AI models, ensuring scalability and performance. Implement REST APIs using SOAP, Flask, Swagger, and Postman.
• Data Visualization:
  Create insightful visualizations and dashboards using tools like Power BI or Tableau.
• MLOps:
  Design and implement MLOps pipelines for continuous integration, continuous deployment (CI/CD), and monitoring of machine learning models. Ensure seamless deployment of models on cloud platforms and maintain robust pipelines for model updates and management.
• Research and Innovation:
  Stay updated with the latest research and advancements in data science, AI, and machine learning to continuously improve solutions.
• Certifications:
  
  Maintain relevant certifications to demonstrate expertise and commitment to professional development.

• Bachelor's or master’s degree in data science, Computer Science, Statistics, or a related field.
• Proven experience in data cleaning, preprocessing, and analysis.
• Strong proficiency in machine learning algorithms, including Random Forest and XGBoost.
• Experience with time series forecasting and predictive analytics.
• Proficient in Python and relevant libraries (scikit-learn, pandas, etc.).
• Knowledge of AI techniques in computer vision, NLP, and generative AI (transformers, BERT). Experience with prompt engineering and RAG applications.
• Experience with cloud platforms (Azure, AWS) and cognitive services.
• Familiarity with SQL and No
  
  SQL databases, including Amazon Redshift and vector stores/DBs.
• Demonstrated ability to develop and deploy ML and AI models. Experience with REST APIs using SOAP, Flask, Swagger, and Postman.
• Proficiency in data visualization tools like Power BI or Tableau.
• Experience with MLOps practices, including CI/CD pipelines and model monitoring.
• Strong…