Data Scientist::Technical Lead

Responsibilities:

• Oversee the development, implementation, and optimization of machine learning models and algorithms for various GenAI projects
• Direct the data preparation process, including data cleaning, preprocessing, and feature engineering, to ensure data quality and readiness for analysis
• Collaborate with data engineers and software developers to streamline data processing and integration into machine learning pipelines
• Evaluate model performance rigorously using advanced metrics and testing methodologies to ensure robustness and effectiveness
• RAG (Retrieval Augmented Generation)(Semantic Kernel, Langchain, LLamaIndex, Cohere )
• Spearhead the deployment of production-ready machine learning applications, ensuring scalability and reliability
• Spearhead the deployment of production-ready machine learning applications, ensuring scalability and reliability
• distributed model training experience
• Design and implement innovative data-driven solutions such as chatbots using the latest GenAI technologies,
• Model fine -tuning experience
• Exposure to various LLM technologies and solid understanding of Transformer Encoder Networks.
• Able to apply deep learning and generative modeling techniques to develop LLM solutions in the field of Artificial Intelligence.
• Communicate complex data insights and recommendations to senior stakeholders through compelling visualizations, reports, and presentations
• Lead the adoption of cutting-edge GenAI technologies and methodologies to continuously improve data science practices.
• Champion knowledge sharing and skill development within the team to foster an environment of continuous learning and innovation.

Qualifications:

• Bachelor s or Master s degree in Data Science, Computer Science, Statistics, or a related field.
• 4-10 years of relevant experience in data science, with significant expertise in GenAI projects.
• Advanced programming skills in Python, R, or Scala, and proficiency in machine learning libraries like TensorFlow, PyTorch, or scikit-learn.
• Extensive experience in data preprocessing, feature engineering, and statistical analysis.
• Demonstrated leadership in managing data science teams and projects.
• Exceptional problem-solving, analytical, and project management skills.
• Strong knowledge of cloud computing platforms such as AWS, Azure, or Google Cloud, and data visualization techniques.