Lead AI/ML Engineer

Role Overview - Lead Data Scientist:

We are seeking a Senior Data Scientist with 5+ years of experience to lead and deliver impactful data science solutions. The ideal candidate should possess deep expertise in machine learning algorithms (GLM, Neural Networks) which include EDA, feature engineering, and proficiency in coding using Python, SQL, and be comfortable on cloud-based platforms like Databricks (must). This role emphasizes strong conceptual understanding, clear interpretation and communication of outcomes, team building, and talent development. Experience with big data platforms, neat documentation, coding best practices, collaborative development using GitHub, and robust model monitoring is essential.

Responsibilities:

• Collaborate with stakeholders to define business problems and translate them into data science projects.
• Gather, clean, and preprocess data from multiple sources using Python and SQL on platforms like Databricks.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and data quality issues.
• Engineer features to enhance model performance and interpretability.
• Select, develop, and validate machine learning models (GLM, GenAI, neural networks).
• Monitor model performance post-implementation, ensuring accuracy, stability, and business relevance over time.
• Maintain clear, organized, and comprehensive documentation for all data science processes, code, and methodologies.
• Use GitHub for version control, code review, and collaborative development.
• Work closely with business partners to gather requirements and ensure alignment on project goals.
• Present complex technical findings in a clear, actionable manner to both technical and non-technical audiences.
• Provide recommendations based on model insights to drive business decisions.
• Foster a collaborative, inclusive, and high-performing team environment.
• Mentor and coach junior data scientists, supporting their technical and professional growth.
• Lead knowledge sharing sessions and promote best practices in data science and analytics.