Strategic Project Lead, Sciences

Mecka•New York, NY

About The Position

Mecka AI is building the data and deployment infrastructure for embodied intelligence. We collect, curate, and license the world's most useful robotics training data to leading AI labs, and we deploy real robotic systems with enterprise customers across hospitality, retail, QSR, pharmacy, logistics, and healthcare. We work with the foundation model teams shaping the next decade of robotics, and with the operators running real businesses today. Quality, trust, and execution are core to our partnerships. The Role We're hiring a Strategic Project Lead, Sciences to own scientific data acquisition programs end-to-end for AI-lab customers. You will scope the work with the customer, recruit and manage scientific experts, design the data collection methodology, own quality, and ship datasets that are useful for frontier model training and evaluation. This is a senior individual contributor role at the intersection of customer engagement, scientific operations, and data quality. You should be quantitative, hands-on, and comfortable turning ambiguous research needs into operational programs that produce trustworthy data.

Requirements

Scientific depth: 5+ years across scientific research, research operations, scientific data programs, technical program management, or a related field.
Domain fluency: Deep working knowledge of a technical or scientific research domain.
Quantitative ability: Comfortable with experimental design, statistical reasoning, operational metrics, and data-driven decision-making.
Project ownership: Track record owning complex, cross-functional programs with external stakeholders and hard delivery dates.
Customer-facing judgment: You can build trust with technical customers, clarify ambiguous asks, and communicate risk without hiding the hard parts.

Nice To Haves

Experience running scientific data collection, benchmarking, research operations, or research programs with many contributors.
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, including knowing what good work looks like in a given domain.
Comfort operating in spreadsheets, SQL, Python, statistics tools, or BI dashboards when the problem calls for it.
Builder mentality: you write the protocol the first time, tighten it after the pilot, and turn it into a system by the third run.

Responsibilities

Customer Engagement Scientific scoping: Work directly with AI labs and research teams to translate model needs into data acquisition programs across specialized technical and scientific domains.
Account ownership: Serve as the day-to-day owner for your customer program — timelines, risks, deliverables, quality, and trust all sit with you.
Technical translation: Convert open-ended scientific requirements into clear protocols, acceptance criteria, and operating plans that internal teams and external experts can execute.
Customer narrative: Communicate tradeoffs clearly to customer stakeholders: what data is feasible, what will take longer, where quality risk exists, and what should be prioritized next.
Data Collection Methodology Protocol design: Design scientific data collection workflows that produce consistent, auditable, model-useful outputs.
Expert network buildout: Recruit, evaluate, and manage specialized domain experts and technical contributors.
Measurement rigor: Define what good data means for each program: experimental setup, metadata, controls, sampling plans, review rubrics, and failure modes.
Quantitative analysis: Use data, statistics, and operational metrics to identify bottlenecks, quality drift, and opportunities to improve collection throughput.
Quality & Execution Dataset delivery: Own the path from first pilot to production dataset, including staffing, timelines, QA, escalation, customer review, and final delivery.
Quality systems: Build quality checks that catch scientific, procedural, and annotation errors before data reaches the customer.
Cross-functional execution: Partner with data operations, engineering, product, legal, finance, and recruiting to remove blockers and keep programs moving.
Operating cadence: Run the weekly operating rhythm: dashboards, customer updates, expert performance reviews, issue logs, and postmortems.
Program Scaling Repeatable playbooks: Turn successful pilots into repeatable scientific data collection playbooks that can scale across customers and domains.
Vendor and lab coordination: Manage external labs, contractors, equipment constraints, sample logistics, compliance considerations, and documentation requirements where needed.
Domain expansion: Identify adjacent scientific data opportunities and help Mecka build the operating muscle to serve them.
Internal standards: Raise the bar for how Mecka scopes, collects, reviews, and ships scientific datasets.