We're 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.
\nThis 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.
\nThe 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.
\nResponsibilities: 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
\nRequirements: 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
\nPreferred 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
\nWhat 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.
\nCompensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
\nSalary Range: $167,200-$209,000 USD
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\nMicrosoft AI are looking for a talented Principal Applied Scientist at their Redmond office. This role sits at the heart of scientific and technical strategy for data-driven attribution and causal measurement across advertising systems. You will help define the future of data-driven attribution and causal measurement, shaping the methodologies that determine how value is estimated and optimized across the ecosystem.
\nAbout the Role
\nAs a Principal Applied Scientist, you will help define the scientific and technical strategy for data-driven attribution (DDA) and causal measurement across advertising systems. You will establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modeling, and bias correction in environments with partial observability. You will lead the design and production adoption of attribution and causal inference frameworks that improve bidding, ranking, optimization, and advertiser ROI at web scale. You will set evaluation standards that distinguish correlation from causation and elevate experimental rigor across teams. You will identify capability gaps and introduce advanced research, tools, or modeling approaches to strengthen measurement foundations. You will operate across organizational boundaries to align research, engineering, product, and business leaders on measurement strategy.
\nAccountabilities
\nThe Candidate we're looking for
\nExperience:
\nTechnical skills:
\nPersonal attributes:
\nBenefits
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\nMicrosoft AI are looking for a talented Principal Applied Scientist at their Sunnyvale office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising the advertising marketplace. You'll work directly with leadership to shape the company's direction in the development of large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback.
\nAbout the Role
\nOur Signals Modeling team builds the intelligence that powers how the advertising marketplace understands user behavior, measures impact and optimizes outcomes from initial impressions through downstream conversions and long-term advertiser value. We develop large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback, enabling principled decision-making across ranking, bidding, pricing, and budget allocation. Our work sits at the foundation of marketplace optimization, where accurate attribution and measurement directly influence billions in advertising spend. The team designs and operates state-of-the-art modeling platforms spanning representation learning, weak-supervision, multi-objective training, calibration, and rigorous experimentation. We transform sparse engagement signals into reliable learning targets and build models that remain robust under delayed conversions, selection bias, and rapidly shifting marketplace dynamics.
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\nThe Candidate we're looking for
\nExperience:
\nTechnical skills:
\nPersonal attributes:
\nBenefits
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