Lead Data Scientist (ML/GenAI)

Lead Data Scientist - AI & Machine Learning

Position Overview

We are looking for a Lead Data Scientist to drive the development, implementation, and optimization of AI and machine learning models, with a focus on generative AI, large language models (LLMs), and predictive analytics. This role will play a strategic leadership role, guiding data-driven decision-making and ensuring that AI solutions are effectively integrated into business processes.

As a technical leader, you will architect AI/ML solutions, mentor junior data scientists, and collaborate with business leaders, engineers, and product teams to turn raw data into meaningful insights. The ideal candidate will have extensive hands-on experience in AI model development, data engineering, and statistical analysis, as well as the ability to translate complex technical concepts into actionable business strategies.

Key Responsibilities

• Lead the development and deployment of AI/ML models, ensuring alignment with business objectives.
• Oversee data science strategy, providing thought leadership on best practices, emerging trends, and innovation in AI/ML.
• Collaborate with cross-functional teams, including product managers, engineers, and business leaders, to identify AI-driven opportunities.
• Architect scalable AI pipelines, incorporating state-of-the-art techniques such as retrieval-augmented generation (RAG), LLM optimization, and predictive modeling.
• Mentor and guide junior data scientists, fostering a culture of continuous learning and technical excellence.
• Implement MLOps best practices, ensuring robust model monitoring, retraining, and lifecycle management.
• Communicate AI-driven insights effectively, presenting findings to stakeholders in both technical and non-technical terms.

Required Skills & Experience

• 5+ years of experience in data science, AI, or machine learning, with a proven track record of leading AI projects.
• Proficiency in machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, and XGBoost.
• Deep understanding of LLMs and generative AI techniques, including RAG, vector databases, and model fine-tuning.
• Experience in AI/ML deployment on cloud platforms (AWS, Azure, or Google Cloud Platform).
• Strong programming skills in Python, SQL, and data engineering tools.
• Expertise in data pipeline development, feature engineering, and large-scale data processing.
• Proficiency in MLOps principles, including orchestration tools, CI/CD for ML, and model monitoring.
• Proven ability to lead and mentor teams, fostering collaboration and technical growth.

Preferred Skills & Industry Experience

• Familiarity with LangChain, LlamaIndex, Haystack, Azure AI Studio, and retrieval-augmented generation pipelines.
• Experience working with big data frameworks (Hadoop, Spark) for large-scale data handling.
• Background in financial services, revenue cycle management, or collections is a plus but not required.

Desired Qualifications

• Master's or PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent industry experience.
• Proven experience leading AI teams or large-scale AI initiatives.