About the Role
At Scale AI, we help leading enterprises turn AI from a promising capability into reliable systems that improve real workflows and deliver measurable business value. We are hiring a Senior Forward Deployed Data Scientist / Engineer to work directly with customers on ambiguous, high-impact problems at the intersection of data science, product development, and AI deployment.
This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production. In many cases, the data scientist builds the first usable version of the solution, proves value quickly, and helps drive it into a durable product or platform capability.
The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.
What You’ll Do
- Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria
- Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans
- Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems
- Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration
- Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations
- Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement
- Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline
- Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production
- Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities
What We’re Looking For
- 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role
- Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact
- Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans
- Strong foundation in statistics, experimentation, causal reasoning, and measurement
- Experience building tools or products, not just analyses — for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications
- Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work
- Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly
- Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives
- High ownership and bias toward shipping in fast-moving environments with incomplete information
Preferred Qualifications
- Experience in a forward deployed, solutions, consulting, or other client-facing technical role
- Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products
- Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow
- Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery
- Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems
- Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling
- Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign
What Success Looks Like
Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.