About Us

Alembic is the pioneering Causal AI platform. We help the world's largest enterprises move past correlation to prove what actually drives business outcomes — the question marketing and growth teams have never been able to answer with confidence. Fortune 100 companies including Nvidia, Delta Air Lines, and Mars use Alembic to make multimillion-dollar decisions on trusted, causal evidence.

We're backed by a $145M Series B from WndrCo (founded by Jeffrey Katzenberg), Jensen Huang, Joe Montana, Prysm Capital, and Accenture. Our models run on our own NVIDIA DGX SuperPOD built on Grace Blackwell infrastructure — one of the fastest private supercomputers in the world. (We've melted GPUs getting here.)

About the Role

We're hiring a Senior Field Scientist in Chicago to lead the data implementations that prove Alembic's value to the world's largest marketers. You'll design and execute field studies on real customer data, apply rigorous analytical methods, and present findings to the executives who own the spend. As a senior member of the team you'll also mentor junior field scientists and help evolve how we do this work.

This seat is in Chicago because a concentration of our most strategic enterprise accounts is here, and this work does not happen over video. You'll be in the room for the data-requirements conversations, the readouts, and the moments where a study changes how a Fortune 100 marketing organization allocates its budget.

If you've spent your career inside marketing mix modeling, multi-touch attribution, or media measurement — at a measurement firm, a holding-company agency, or a Fortune 100 brand's analytics group — you already know where correlation-based attribution runs out of road. This is the chance to work on what replaces it.

What You'll Do

Research and Analysis

• Design and execute complex field studies that test hypotheses about marketing effectiveness and validate Alembic's transformational value.

• Lead data collection and requirements gathering with senior customer stakeholders, treating each engagement as a scientific research opportunity.

• Apply rigorous analytical methodology to prepare and process customer data, ensuring scientific validity at enterprise scale.

• Develop and test hypotheses about marketing performance, using experimental design principles to structure customer engagements.

Research Communication and Collaboration

• Present findings to C-level and senior customer stakeholders, translating complex analytical results into business impact.

• Serve as the scientific presence on the ground for Chicago-area accounts — building the working relationships with analytics, media, and finance teams that let a study actually get the data it needs.

• Collaborate with Enterprise AEs and Product to align research methodology with strategic objectives and ensure scientific rigor supports the sales process.

• Drive scalable research processes and best practices across the field science team.

• Mentor and develop junior field scientists in experimental design and analytical method.

Product and Process Innovation

• Contribute to product roadmap discussions based on customer technical feedback and research outcomes.

• Establish frameworks for replicable field studies that can be applied systematically across engagements.

What Will Help You Succeed

Core Research and Technical Skills

• 5–8+ years in sales engineering, data science, analytics consulting, or a similar technical pre-sales role in B2B SaaS, data, cloud, or AI.

• A track record leading research implementations that validated a complex enterprise solution and moved a deal to close.

• Strong data analysis with scientific rigor — you can design an experiment and process large-scale enterprise data sets without hand-waving the assumptions.

• Advanced Python (or equivalent) for data preparation, quality testing, and statistical analysis.

Domain Expertise

• Hands-on experience with marketing measurement — marketing mix modeling, multi-touch attribution, incrementality and lift testing, geo experiments, or holdout design — and a clear-eyed view of what each method can and can't establish.

• Deep experience with data platforms, cloud infrastructure, or AI solutions.

• Excellent communication and presentation skills for sharing findings with C-level executives.

• Strong problem-solving mindset and scientific thinking.

• Collaborative, proactive, with team leadership experience.

Preferred Qualifications

• Background at a measurement or market-research firm, a media or creative agency, or the analytics function of a large advertiser.

• Field studies or research implementations for data, cloud, or AI in a marketing context.

• Prior work as a senior data analyst, business analyst, or technical consultant with experimental design experience.

• Familiarity with enterprise procurement and security review processes.

• Startup or high-growth experience scaling research methodology.

• Advanced technical certifications (AWS Solutions Architect, GCP Professional) or expert-level AI/ML training.

• Exposure to value engineering, ROI modeling, and business case development.

• Previous startup or founder experience.

The role is right for you if

• You're a seasoned field scientist ready to lead research implementations of genuinely new technology — proving transformational value to Fortune 500 marketing teams through rigorous method, and building the research processes that scale it.

• You want work that is both scientifically challenging and tied to a real customer need, with major upside against a massive market, while mentoring the next generation of analytical talent.

Why You Might Be Excited About Alembic

• High-impact research with elite clients: You'll run field studies with Fortune 100 companies making multimillion-dollar marketing decisions — studies that matter, for clients that move the industry.

• A product that validates itself: You're not pushing features. You're running analysis that demonstrates 10–100x improvements and makes sophisticated prospects ask "how is this scientifically possible?"

• The method is the moat: Next-generation attribution grounded in mathematical physics — dynamical systems applied to messy enterprise data — running on one of the world's fastest private supercomputers.

• First-mover advantage: You'll be proving something that has never existed before, which is a real edge in every analytical conversation you have.

• Series B momentum, real ownership: Meaningful equity at a Series B company that's raised $145M, with proven product-market fit and Fortune 100 traction.

Why You Might Not Be Excited

• You'd rather demonstrate an established, commoditized product with a well-defined playbook than do consultative scientific work that has to earn belief every time.

• You want a fully built-out research process rather than helping define our field study methodology through actual experimentation.

• You prefer static technical demos over dynamic research that adapts to what the data and the market tell us.

• You want a fully remote role. This one is deliberately local — the in-person time with customers is the job, not a formality.