# Senior Field Scientist

**Company:** [Alembic](https://hotfix.jobs/companies/alembic-2)
**Location:** San Francisco, CA
**Role:** Sales Engineering
**Salary:** $160k – $185k/yr
**Experience:** 5+ years
**Skills:** Python, Data Analysis, Statistical Analysis, Experimental Design, AWS, GCP, AI, Cloud Infrastructure, Data Platforms, Roi Modeling
**Posted:** 2026-04-29

> Leads design and execution of field studies using customer data to validate marketing effectiveness of AI, cloud, and data solutions. Presents findings to C-level stakeholders, mentors juniors, and drives research methodology in pre-sales for enterprise clients. Requires 5+ years in sales engineering or data science with strong Python and analytical skills.

## Job Description

## What You'll Do

### Research & Analysis
- Design and execute complex field studies that test hypotheses about marketing effectiveness, validating 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 methodologies to prepare and process customer data, ensuring scientific validity for enterprise-scale research implementations
- Develop and test hypotheses about marketing performance, using experimental design principles to structure customer engagements

### Research Communication & Collaboration
- Present compelling research findings to C-level and senior customer stakeholders, translating complex analytical insights into business impact
- Collaborate with Enterprise AEs and Product teams to align research methodologies 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 proper experimental design and analytical methodologies

### Product & Process Innovation
- Contribute to product roadmap discussions based on customer technical feedback and research outcomes
- Establish frameworks for replicable field studies that can be systematically applied across customer engagements

## What Will Help You Succeed

### Core Research & Technical Skills
- 5-8+ years in Sales Engineering, Data Science, or similar pre-sales technical roles in B2B SaaS, data, cloud, or AI
- Proven track record leading successful research implementations that validated complex enterprise solutions and progressed deals to close
- Strong data analysis skills with scientific rigor, including ability to design experiments and process large-scale enterprise data sets
- Advanced proficiency in **Python** and/or other scripting languages for data preparation, quality-testing, and statistical analysis

### Domain Expertise
- Deep experience with data platforms, cloud infrastructure, or AI solutions
- Excellent communication and presentation skills for sharing research findings with C-level executives
- Strong problem-solving mindset and scientific thinking ability
- Collaborative and proactive approach with team leadership experience
- Ability to travel occasionally

### Preferred Qualifications
- Experience conducting field studies or research implementations for data, cloud, or AI in marketing contexts
- Background as a senior data analyst, business analyst, or technical consultant with experimental design experience
- Deep familiarity with enterprise procurement and security review processes
- Startup or high-growth environment experience with scaling research methodologies
- Advanced technical certifications such as **AWS Solutions Architect** or **GCP Professional**
- Ability to create and manage scalable research frameworks and reusable analytical assets
- Experience working with marketing customers and understanding their technical landscape
- Previous startup or founder experience
- Expert-level technical training or certifications in AI/ML
- Experience contributing to product strategy and roadmap decisions based on research outcomes
- Advanced exposure to value engineering, ROI modeling, and business case development through scientific analysis

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