Lead ML Scientist

Lead ML Scientist

Requirements

• Experience applying AI, ML & statistical modeling on large datasets; pre-processing, data quality, feature engineering; the whole nine-yards.
• Deep understanding of the basic classes of ML algorithms, statistics & linear algebra; with a keen interest in the underlying theory & assumptions.
• Experience building AI & LLM based applications in a business environment; up to date with advancements in the Gen-AI space, in an applied sense with techniques such as RAG, in-context learning, search, agentic workflows etc.
• Solid understanding of NLP, embedding methods, foundational models and model fine-tuning; with experience in developing conversational & agentic AI.
• Experience writing production-quality, scalable code using Python. Strong with the software development lifecycle and its best practices.
• Experience managing AI/ML project lifecycles; with a point-of-view for designing and developing end-to-end turn key ML solutions, enablement & adoption baked into the solution. Also experience iterating on ideas and deliverables.
• Strong stakeholder management skills; with the ability to articulate results and complex concepts to all audiences. Also the ability to work with leadership.
• Proven experience leading and mentoring AI/ML teams; demonstrate leadership, with the ability to inspire the team & lead a project to completion.

Responsibilities

• Lead projects; Conceptualize, design and lead the execution of large AI & ML projects, aligning with the business goals, delivering measurable value & impact.
• Engage with stakeholders; lead the conversations with technical & non-technical stakeholders; identify areas where one can apply AI & ML; facilitating discussions around key drivers for decision making & operational efficiencies.
• Provide technical & thought leadership; designing and implementing advanced machine learning techniques, focusing on robust & scalable solutions. Participate in all stages of development, ideation to production.
• Provide mentorship; to junior ML & Data Scientists, around problem formulation, technical design, algorithm choices & code quality.
• Develop hands-on; analyze large amounts of data, manage data quality, design & develop complex ML models (and the ensuing ML solutions) including ML pipelines, deploy & manage production-grade applications end-to-end, and tell the story in a compelling manner.
• Partner with customers; showcase internal solutions to customers, help them envision solutions stringing Snowflake platform capabilities together, partner with internal teams that enable customer solutions.
• Set technical direction and strategy; by defining the roadmap and aligning it with business goals. Also guiding the team in prioritizing projects for impact.
• Oversee team execution and deliverables; to ensure high-quality execution by managing team workflows, reviewing deliverables and optimizing the ensuing processes.

Preferred Qualifications

• High levels of curiosity, eager enthusiasm & demonstrable experience working on open-ended problems. Exceptional problem solving skills with the ability to think strategically. Demonstrable experience and interest in problem formulation based on relatively abstract information.
• High levels of adaptability, to respond to fast-evolving project scope, adjusting strategies & plans while delivering value; comfortable being scrappy.