Forward Deployed Data Analyst

Requirements

• Quantitative degree (mathematics, statistics, economics, physics, computer science, psychology, or related field)
• 2-5 years in data analysis, market research, consulting, or customer-facing technical roles
• Excellence at translating complex technical concepts for non-technical executives
• Proven ability to manage sophisticated customer relationships independently
• Thrives in ambiguous, fast-moving startup environments
• Proficient in Python
• Quick learner who can master new AI/ML platforms and concepts
• Be able to identify root cause of technical issues in data and evaluation pipelines
• (Desirable) Experience with LLMs, AI systems, or enterprise SaaS implementations
• (Desirable) Management consulting background, especially in technology transformation
• (Desirable) Track record presenting to C-suite executives
• (Desirable) Knowledge of behavioural science or consumer insights
• High levels of self-discipline and organisation
• Intellectually curious with exceptional attention to detail
• Builds trust quickly with senior stakeholders
• Comfortable with high-stakes decisions and broad responsibilities
• Strong ownership mentality with composure under pressure

What the job involves

• As a Forward Deployed Analyst, you’ll be the critical bridge between our AI platform and the end user. Working directly with clients, you’ll configure synthetic populations that mirror their real-world audiences and help them extract maximum value from behavioural insights
• This high-impact, customer-facing technical role operates at the intersection of AI, data science, and strategic consulting – ideal for someone with a quantitative/data science background looking to move into AI products
• Transform Datasets to Onboard onto our Product: Design synthetic population queries by onboarding custom datasets for customers
• Improve Data Pipelines: Diagnose data issues and partner with engineering to enhance platform capabilities
• Build confidence through rigour: Evaluate synthetic methodologies using established validation frameworks to build client trust in AI insights for high-stakes decisions
• Lead technical engagements: Own the technical dialogue with customers—understand their data, design optimal solutions, and implement them collaboratively
• Scale adoption: Enable customers to unlock full product value across the whole organisation through effective training, documentation, and use case storytelling
• Shape our product: Represent the voice of the customer – gather field feedback and inform product priorities based on real implementation patterns

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