Qualifications & Skills Required:
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 NoSQL 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 PowerBI or Tableau.
Experience with MLOps practices, including CI/CD pipelines and model monitoring.
Strong research skills and ability to stay current with industry trends.
Relevant certifications in data science, machine learning, or cloud computing.