Data and Machine Learning Developer

Propose valuable business solutions using statistical and AI/ML models.

Model Design: Design statistical models and machine learning/deep learning models to address complex business challenges.

Model Deployment: Develop and deploy ML/DL models into production environments.

Data Insights: Formulate available data sources, augment them, and extract valuable insights using SQL and efficient query writing techniques.

Data Visualization: Create innovative data visualizations and graphs using tools like d3js, dashplotly, and neo4j.

Ensure seamless integration of AI/ML models into business processes using cloud platforms like AWS or Azure.

Cross-Functional Collaboration: Work closely with cross-functional teams, translating business needs into actionable AI solutions.

Agile Participation: Participate in agile project delivery, ensuring the timely execution of deliverables.

Python Programming: Proficient in Python programming.

Practical knowledge of Statistics and Operations Research methods.

Hands-on experience with frameworks and tools such as Flask, PySpark, PyTorch, TensorFlow, Keras, Databricks, OpenCV, Pillow/PIL, Streamlit, and neo4j.

Analytics/AI-ML Services: Expertise in Analytics/AI-ML AWS services, including SageMaker, Canvas, Bedrock.

Predictive Modeling: Strong understanding of predictive modeling techniques like regression models, XGBoost, random forests, GBM, Neural Nets, SVM, etc.

Proficient in NLP techniques like RNN, LSTM, and Attention-based models, with knowledge of Stanford NLP, IBM, Azure, and OpenAI NLP models.

MLOps: Experience in MLOps: deploying models into production on AWS or Azure cloud platforms.

AWS Specialty Certification in Data Analytics or Machine Learning.