Strategy Consultant – AI Training & Evaluation (MBB & Top-Tier Firms)

Remote from: Spain
Salary, yearly, USD: 120,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 29 May 2026
Apply before: 29 Jun 2026
Experience level: Midweight
Views / Applies: 20 / 2

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring

AI Summary

This role involves building domain-specific reinforcement learning environments and evaluation frameworks for AI training, targeting consultants from top-tier strategy firms like McKinsey, BCG, and Bain. You will create realistic consulting project scenarios, design structured tasks for AI agents, and define evaluation criteria. The position is remote, project-based, and requires 3+ years of experience at eligible firms. Compensation is up to $60 per hour, equivalent to an estimated annual salary based on typical consulting rates.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires high-level strategic thinking and problem-solving skills typical of top-tier consulting, but the tasks are well-defined and project-based, making it challenging but not extreme.

Salary Analysis

AI Insight The offered hourly rate of $60 per hour equates to approximately $120,000 annually, which is at the lower end for strategy consultants with top-tier firm experience. The market range for such roles is typically $100k-$200k, so the compensation is competitive for a project-based, remote role.

Key Skills

Strategy Consulting AI Training Project Design Evaluation Frameworks Problem Structuring Financial Modeling Market Analysis Client Delivery Remote Work Top-Tier Firms

Cover Letter Sample

I am writing to express my interest in the Strategy Consultant - AI Training & Evaluation position. With over 5 years of experience at McKinsey & Company, I have developed strong skills in structuring ambiguous problems, building financial models, and delivering client-ready recommendations under tight deadlines.

I am excited about the opportunity to apply my consulting expertise to shape how AI learns high-level business reasoning. My background in commercial due diligence and growth strategy aligns perfectly with the need to create realistic project scenarios and evaluation frameworks.

I am particularly drawn to the remote, project-based nature of this role, which allows for independent work while contributing to cutting-edge AI initiatives. I am confident in my ability to design tasks that mirror real consulting engagements and define rigorous evaluation criteria.

Thank you for considering my application. I look forward to the possibility of contributing to your team and helping advance the capabilities of generative AI models.

Possible Interview Questions

Describe a time you structured an ambiguous client problem into a workable analytical plan. What steps did you take?

At BCG, I was given a vague request to 'improve profitability' for a retail client. I started by defining the problem scope, then broke it into hypotheses: revenue growth vs. cost reduction. I created a work plan with key analyses: customer segmentation, margin analysis, and competitor benchmarking. I assigned timelines and deliverables, and iterated with the client to refine the focus.

How would you design a task for an AI agent to perform a market sizing analysis?

I would provide a scenario: a client wants to enter the electric vehicle charging market in Germany. The task would include key assumptions (e.g., population, EV adoption rates), data sources (e.g., government reports), and constraints (e.g., budget, timeline). The AI must calculate total addressable market, segment by charging type, and justify its methodology. I would also specify output format and evaluation criteria.

How do you ensure logical consistency in your deliverables when faced with incomplete information?

I use hypothesis-driven thinking: start with a structured hypothesis, then test it with available data. I document assumptions clearly and assess their impact on conclusions. For missing data, I use proxies or triangulate from multiple sources. I also review the logic chain for internal consistency and sanity-check results against industry benchmarks.

Can you provide an example of a grading rubric you would create for a cost optimization task?

The rubric would have criteria: 1) Problem understanding (10%): correctly identifies cost drivers. 2) Analysis depth (30%): uses appropriate tools like activity-based costing. 3) Recommendations (30%): actionable, with impact estimation. 4) Communication (20%): clear structure, logical flow. 5) Assumptions (10%): explicit and reasonable. Each criterion has a 1-5 scale with descriptors.

How do you handle conflicting inputs from different stakeholders when building a project scenario?

I would create a scenario where the CEO wants growth but the CFO wants cost cuts. I'd frame the task as a strategic trade-off: the AI must analyze both perspectives, quantify trade-offs, and propose a balanced recommendation. I'd provide conflicting data (e.g., market growth vs. margin pressure) and require the AI to justify its prioritization.

Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.

Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives. Backed by Toloka’s deep expertise in scalable data generation, crowd technology, and applied ML systems, Mindrift enables experts to shape how next-generation generative models learn, reason, and perform.

We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems. To do this credibly, we are assembling a team of strategy consultants from top-tier firms who can convert authentic project experience into end-to-end examples — from problem structuring and work planning to analysis, synthesis, and client-ready recommendations.

You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.

Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm. If you do not have hands-on project experience at one of the firms listed below, please do not apply. This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.

Eligible firms: McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, Oliver Wyman, Roland Berger, Monitor Deloitte (Deloitte S&C), EY-Parthenon, Kearney, and Strategy& (PwC).

Who We’re Looking For

Consultants with 3+ years of experience at one of the firms listed above, with hands-on project experience in:

Structuring ambiguous client problems into workable analytical plans
Building financial models, market analyses, or synthesized findings from messy inputs
Producing client-ready deliverables under time pressure
Forming and defending recommendations under uncertainty

No deep technical background is required — we will onboard you on the lightweight tools involved.

What You’ll Do

Build realistic consulting project environments — create detailed project scenarios grounded in real engagement dynamics: industry context, financials, constraints, conflicting inputs, and incomplete information.
Design structured consulting tasks for AI agents — break projects into discrete tasks that mirror real consulting work: market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more.
Define evaluation criteria and quality standards — develop grading frameworks, evaluation rubrics, and golden-answer solutions for each task, used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale.

This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.

Skills & Requirements

3+ years at McKinsey, BCG, Bain, Oliver Wyman, Roland Berger, Monitor Deloitte, EY-Parthenon, Kearney, or Strategy&
Strong structured problem-solving and hypothesis-driven thinking
Ability to translate vague problems into clear analytical steps and deliverables
High attention to logical consistency and output quality
Independent, self-directed working style
Clear written English (B2+)

Compensation

On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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