Configure, maintain, and optimize storage and processing environments (cloud, on-premises, cluster management, etc.)
Build production-grade data and machine learning pipelines that support batch and streaming applications
Advocate for software design best practices and demonstrate your ability to construct robust data and machine learning pipelines
Perform applied research to consistently achieve desired solution performance and improve organizational capabilities
Demonstrate an in-depth understanding of all aspects of learning theory
Support leaders in setting up frameworks for the machine learning development lifecycle and devise machine learning strategies
Advise business leaders in complex settings to create and enable robust machine learning solutions with high impact, at an expert level
Lead and mentor others in all machine learning activities
Utilize deep expertise in LLM fine-tuning and prompt engineering (e.g., OpenAI APIs, Hugging Face, Anthropic Claude, Google Gemini)
Strong experience with AI orchestration tools (e.g., LangChain, LlamaIndex, vector databases for retrieval augmentation)
Hands-on knowledge of function calling and API-based reasoning models (e.g., using structured outputs to drive automated workflows)
Proficiency in Python and AI development frameworks for building scalable AI applications
Understanding of multi-agent architectures and best practices in agentic AI design

Requirements

Bachelor's, Master's, or Ph.D. degree in a technical field or equivalent related professional experience
Expertise in more than one major ML programming language (Python, R, Scala, etc.) and familiarity with others
Experience leading large data science technical programs, delivering successful outcomes typically involving cross-functional teams of 10+
Demonstrated ability to regularly contribute to the data science and technology community through blog posts, tech talks, major data science or technology domain conferences/events, etc.
Experience defining data science best practices at a team/capability level
Expertise in configuring, maintaining, and optimizing storage and processing environments (cloud, on-premises, cluster management, etc.)
In-depth understanding of all aspects of machine learning theory
Strong experience in application in an industrial setting and crafting robust solutions relatively quickly
Solid theoretical foundation applies advanced statistical methods to a broad range of problems
Experience with advanced methods such as Stochastic Processes, Bayesian Neural Networks, Markov Models, Discriminant and Factor Analysis, and applies them while considering underlying assumptions and limitations
Continuous learning and adaptability to stay ahead of rapidly evolving technologies and techniques in machine learning and data science
Strong communication and storytelling skills to effectively convey complex technical concepts to diverse stakeholders
Collaborative mindset and ability to lead cross-functional teams in delivering innovative solutions
Strategic thinking and business acumen to align machine learning initiatives with organizational goals and drive measurable impact
Ethical and responsible AI practices to ensure fairness, transparency, and accountability in machine learning model development and deployment.

Benefits

Medical/dental/vision
Paid time off
Employee Assistance Program
Wellness & travel reimbursement
Travel discounts
International Airlines Travel Agent (IATAN) membership

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonRScalamachine learning pipelinesdata processingLLM fine-tuningprompt engineeringAI orchestration toolsadvanced statistical methodsStochastic Processes

Soft Skills

leadershipcommunicationcollaborationstrategic thinkingadaptabilitystorytellingmentoringethical AI practicesbusiness acumenproblem-solving

Certifications

Bachelor's degreeMaster's degreePh.D. degree