Break Into AI Careers Coach

About the role

Career coaching in this position guides mentees who seek to enter or advance within AI-focused roles by aligning their profiles with industry standards through direct feedback from AI practitioners. The hired coach owns the full candidate journey, translating raw information and business needs into structured coaching routes that lead to competitive AI positions. This role owns the responsibility of designing concrete examples that illustrate practical pathways for candidates succeeding in competitive AI environments. The coach validates recruiting expectations for AI roles to confirm deep, real-world understanding of current hiring standards and quality benchmarks. Another core ownership involves ensuring every remote coaching session satisfies Leland's quality and delivery expectations so that engagement standards remain consistently high. The coach must demonstrate active, real-world industry experience by holding AI-focused roles at established AI companies, which validates their mentor status. Evidence of breaking into competitive AI roles must be provided and shared during coaching to illustrate concrete career pathways for mentees. Ultimately, the coach owns the outcome of helping candidates communicate uncertainty and business impact effectively during data interviews.

Key facts

What you'll do

Conduct structured coaching sessions that adhere to Leland's quality and delivery expectations to meet engagement standards for AI career guidance. Design routes with mentees that produce structured outcomes, mapping competitive pathways to AI roles through tailored coaching plans. Validate recruiting expectations for AI roles by drawing on real-world industry experience and current hiring standards across data teams. Provide concrete examples showing how candidates succeed in competitive AI positions to illustrate practical career pathways and decision-making frameworks. Share evidence of breaking into competitive AI roles during sessions, using personal stories and documented outcomes as coaching material. Translate ambiguous business problems into clear metrics, interpret experiments, and build small models while explaining the business impact to mentees. Coach candidates on typical interview steps, including SQL or coding exercises, statistics questions, and case studies that mirror real company scenarios. Guide candidates to prepare a clean write-up of past analyses, emphasizing how to present projects, communicate uncertainty, and highlight business impact. Ensure remote coaching sessions maintain Leland's quality and delivery expectations by meeting consistent standards of clarity, structure, and practical relevance. Support continuous learning by helping mentees navigate a field that changes quickly, focusing on machine learning, analytics, or infrastructure specialization when appropriate.

Requirements

A bachelor's degree is required; Mentors must hold AI-focused roles at established AI companies to demonstrate active, real-world industry experience that validates their mentorship capability. Mentors must validate recruiting expectations for AI roles to confirm their understanding of real hiring standards and current industry practices. Concrete examples showing how candidates succeed in competitive AI positions must be provided to illustrate practical pathways and decision-making processes. Evidence of breaking into competitive AI roles must be demonstrated through examples shared during coaching engagements. Remote coaching sessions must satisfy Leland's quality and delivery expectations, requiring coaches to meet these standards consistently. Coaches must communicate clearly in English and navigate asynchronous workflows common in remote engagements across USA time zones. Availability during standard business hours for scheduled coaching sessions is necessary to support mentees and maintain engagement quality. Willingness to follow Leland's coaching methodology and quality assurance processes ensures consistency and reliability across all client interactions.

Nice to have

Experience guiding candidates through data interviews that include SQL or coding exercises, statistics questions, and case studies.

Ability to help candidates design metrics, interpret experiments, or build small models while explaining business impact in clear terms.

Familiarity with how companies run take-home analyses and how to coach candidates through them effectively.

Background in translating numbers into decisions for cross-functional work with product and engineering teams at senior levels.

Experience supporting professionals who are transitioning into data careers from non-traditional backgrounds.

Practical notes

Candidates may design metrics, interpret experiments, or build small models while explaining business impact as part of their coaching exercises.

This engagement is conducted remotely with a USA location designation, requiring reliable internet connectivity and a professional workspace.

Travel is not required, and the role is fully distributed across remote platforms and communication tools.

No specific visa sponsorship details are provided, and candidates interested in the coaching work should review eligibility under USA regulations.

Deadlines for coach onboarding or application review cycles are not specified, so interested mentors should contact Leland for current opportunities and timing.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study that reflects real-world scenarios. Candidates may be asked to design a metric, interpret an experiment, or build a small model to demonstrate practical skills. Some companies give a take-home analysis that mirrors actual business problems faced by data teams. Expect questions about past projects and the business impact of your work, focusing on how decisions were made and communicated. Interviewers often evaluate how you communicate uncertainty and business impact, not only the mathematical correctness of the analysis. Bringing a clean write-up of a past analysis to the interview is well received and can highlight communication and structure.

Good to know

Continuous learning is part of the job in a field that changes quickly, requiring mentors to stay updated on tools, methods, and industry standards for AI roles.

Mentors should be comfortable navigating asynchronous communication, documenting coaching outcomes, and maintaining quality across remote sessions.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question that can reveal cultural strengths and performance patterns. Keep the list short and pick the questions that matter most to you to ensure the conversation aligns with your goals.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead, with increasing ownership over complex problems and cross-functional initiatives. Many professionals specialize in machine learning, analytics, or infrastructure, which can shape long-term career pathways within tech organizations. Cross-functional work with product and engineering teams becomes more important at senior levels, requiring stronger communication and influence skills. The field changes quickly, so continuous learning remains essential for staying relevant and competitive in AI-focused roles. Professionals who can translate numbers into decisions tend to advance fastest, as they bridge technical work and business strategy effectively.

About the company

Leland's mission is to unlock human potential by making the world's expertise more accessible, connecting ambitious people with coaching, content, and courses that can help them achieve their career and educational goals through tailored learning experiences.