Strategic Project Lead, Sciences – Toronto

Mecka • Markham, ON

2d

About The Position

Mecka AI is seeking a Strategic Project Lead, Sciences to manage scientific data acquisition programs from start to finish for AI-lab customers. This role involves collaborating with customers to define project scope, recruiting and managing scientific experts, designing data collection methodologies, ensuring data quality, and delivering datasets crucial for frontier model training and evaluation. This is a senior individual contributor position at the intersection of customer engagement, scientific operations, and data quality, requiring a quantitative, hands-on approach to transform ambiguous research needs into operational programs that yield trustworthy data.

Requirements

• 2+ years of experience in scientific research, research operations, scientific data programs, technical program management, or a related field.
• Deep working knowledge of a technical or scientific research domain.
• Comfort with experimental design, statistical reasoning, operational metrics, and data-driven decision-making.
• Proven track record of owning complex, cross-functional programs with external stakeholders and hard delivery dates.
• Customer-facing judgment: ability to build trust with technical customers, clarify ambiguous asks, and communicate risk effectively.
• Direct, low-ceremony, and precise communication style.
• High-agency; ability to move programs forward without waiting for perfect process.
• Detail-oriented approach focused on delivering useful, trusted data on time.
• Calm under pressure when facing challenges like experiment failures, expert attrition, or scope changes.
• Motivation to define how scientific data is produced for embodied AI and frontier models.

Nice To Haves

• Experience running scientific data collection, benchmarking, research operations, or research programs with many contributors.
• Comfort in research environments (labs, field studies, expert interviews).
• Working knowledge of experimental design fundamentals (control conditions, blinding, inter-rater agreement, statistical significance).
• Familiarity with research operations tooling (LIMS, electronic lab notebooks, annotation/eval platforms like Label Studio, Scale, Surge, or research collaboration tools like Notion, Confluence, Quarto).
• Ability to read and summarize research papers (e.g., from arXiv or Google Scholar) and evaluate claims.
• Background at an AI lab, data company, technical software company, or research-heavy startup.
• Familiarity with data annotation, evaluation datasets, expert-in-the-loop workflows, or model training data operations.
• Ability to recruit and assess scientific experts quickly and identify quality work in a given domain.
• Fluency in spreadsheets and modern AI tools for analyzing experimental and program data (sample sizes, variance, reviewer agreement, throughput).
• A builder mentality: experience writing protocols, refining them, and turning them into systems.

Responsibilities

• Customer Engagement: Work directly with AI labs and research teams to translate model needs into data acquisition programs across specialized technical and scientific domains. Serve as the day-to-day owner for customer programs, managing timelines, risks, deliverables, quality, and trust. Convert open-ended scientific requirements into clear protocols, acceptance criteria, and operating plans. Communicate tradeoffs clearly to customer stakeholders regarding data feasibility, timelines, quality risks, and prioritization.
• Data Collection Methodology: Design scientific data collection workflows for consistent, auditable, and model-useful outputs. Recruit, evaluate, and manage specialized domain experts and technical contributors. Define data quality standards for each program, including experimental setup, metadata, controls, sampling plans, review rubrics, and failure modes. Utilize data, statistics, and operational metrics to identify bottlenecks, quality drift, and opportunities for improvement.
• Quality & Execution: Own the entire process from pilot to production dataset delivery, including staffing, timelines, QA, escalation, customer review, and final delivery. Build quality checks to identify scientific, procedural, and annotation errors. Partner with data operations, engineering, product, legal, finance, and recruiting to remove blockers. Manage the weekly operating rhythm, including dashboards, customer updates, expert performance reviews, issue logs, and postmortems.
• Program Scaling: Develop repeatable playbooks for scientific data collection that can scale across customers and domains. Manage external labs, contractors, equipment constraints, sample logistics, compliance, and documentation. Identify adjacent scientific data opportunities and help Mecka build the operating capability to serve them. Raise the internal standards for scoping, collecting, reviewing, and shipping scientific datasets.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed