Data Scientist

Role Definition:

Data Scientists focus on researching and developing AI algorithms and models. In this role, you will analyze data, build predictive models, and apply machine-learning techniques to solve complex problems.

Key Responsibilities:

• Research and develop AI algorithms and predictive models.
• Analyze data to extract actionable insights.
• Build, test, and deploy machine learning and deep learning models.
• Manage end-to-end AI solution delivery, from data mining and cleaning to deployment.
• Collaborate with cross-functional teams to integrate AI solutions into business processes.
• Ensure effective monitoring and maintenance of data pipelines and model performance.

Required Skills:

Proficient:

• Languages/Frameworks: Fast API, Azure UI Search API (React)
• Databases and ETL: Cosmos DB (API for MongoDB), Data Factory, and Data Bricks
• Programming: Proficiency in Python and R
• Cloud: Azure Cloud Basics (Azure DevOps)
• Version Control & CI/CD: Gitlab Pipeline
• Deployment: Ansible and REX (Rex Deployment)
• Data Science: Prompt Engineering, Modern Testing, and data mining/cleaning
• Machine Learning: Supervised/unsupervised learning, NLP techniques, and familiarity with deep learning techniques (RNN, transformers)
• End-to-End Delivery: AI integration, deployment, and model deployment processes
• Frameworks & Operations: AI frameworks (PyTorch), MLOps frameworks, and data pipeline monitoring

Expert (in addition to the proficient skills):

• Languages/Frameworks: Expertise in Azure Open AI
• Data Science: Proficiency with Open AI GPT Family of models (4o/4/3), embeddings, and vector search
• Databases and ETL: Experience with Azure Storage Account
• Machine Learning: Expertise in a broad range of machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Deep Learning: Proficiency in deep learning frameworks (TensorFlow, PyTorch)
• Mathematics: Strong mathematical foundation in linear algebra, calculus, probability, and statistics
• Research: Solid research methodology and experimental design skills
• Data Analysis: Proficiency in data analysis tools (Pandas, NumPy, SQL)
• Statistics: Strong statistical and probabilistic modeling skills
• Data Visualization: Experience with tools like Matplotlib, Seaborn, or Tableau
• Big Data: Knowledge of big data technologies such as Spark and Hive
• Analytics: Experience with AI-driven analytics and decision-making systems