Data Scientist

• Design, develop, and implement advanced AI and machine learning models focusing on generative AI and NLP technologies.

• Work with large datasets, applying statistical and machine learning techniques to extract insights and develop predictive models.

• Collaborate with engineering teams to integrate models into production systems.

• Apply best practices for model training, tuning, evaluation, and optimization.

• Develop and maintain pipelines for data ingestion, feature engineering, and model deployment.

• Leverage tools like OpenAI s GPT models, Google Gemini, Microsoft Copilot, and other available platforms for AI-driven solutions.

• Build and experiment with large language models, recommendation systems, computer vision models, and reinforcement learning systems.

• Continuously stay up-to-date with the latest AI/ML technologies and research trends.

• Proven experience as a Data Scientist, Machine Learning Engineer, or similar role.

• Strong expertise in building and deploying machine learning models across various use cases.

• In-depth experience with AI frameworks and tools such as OpenAI (e.g., GPT models), Google Gemini, Microsoft Copilot, and others.

• Proficiency in machine learning techniques, including supervised/unsupervised learning, reinforcement learning, and deep learning.

• Expertise in model training, fine-tuning, and hyperparameter optimization.

• Strong programming skills in Python, R, or similar languages.

• Solid understanding of model evaluation metrics and performance tuning.

• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and ML model deployment tools like TensorFlow, PyTorch, and Keras.

• Experience with MLOps tools such as MLflow, Kubeflow, and DataRobot.

• Strong experience with data wrangling, feature engineering, and preprocessing techniques.

• Excellent problem-solving skills and the ability to communicate complex ideas to non-technical stakeholders.

• PhD or Master s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

• Experience with large-scale data processing frameworks (Hadoop, Spark, Databricks).

• Expertise in Natural Language Processing (NLP) techniques and frameworks like Hugging Face, BERT, T5, etc.

• Familiarity with deploying AI solutions on cloud services, including AWS SageMaker, Azure ML, or Google AI Platform.

• Experience with distributed machine learning techniques, multi-GPU setups, and optimizing large-scale models.

• Knowledge of reinforcement learning (RL) algorithms and practical application experience.

• Familiarity with AI interpretability tools such as SHAP, LIME, and Fairness Indicators.

• Proficiency in using collaboration tools such as Jupyter Notebooks, Git, and Docker for version control and deployment.

• Natural Language Processing (NLP): OpenAI GPT, BERT, T5, spaCy, NLTK, Hugging Face

• Machine Learning Frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn

• Big Data Processing: Hadoop, Spark, Databricks, Dask

• Cloud Platforms: AWS SageMaker, Google AI Platform, Microsoft Azure ML, IBM Watson

• Automation Deployment: Docker, Kubernetes, Terraform, Jenkins, CircleCI, GitLab CI/CD

• Visualization Analysis: Tableau, Power BI, Plotly, Matplotlib, Seaborn, NumPi, Pandas

• Database : RDBMS, NoSQL

• Version Control: Git, GitHub, GitLab