Forward Deployed Principal Data Scientist

SAP•New York, NY

2d•$176,300 - $374,200•Hybrid

About The Position

In this role, you design, develop, and apply advanced data science, statistical, and machine learning methods to solve complex, business-critical problems. Embedded directly within customer environments and fast-moving delivery teams, you translate strategic and operational challenges into rigorous analytical solutions at speed. You work closely with customers to deliver scalable, enterprise-grade outcomes that drive measurable business value. You are accountable for the quality, impact, and delivery of complex analytical initiatives — acting as a subject-matter expert, managing ambiguity, and ensuring data-driven solutions are robust, reliable, and aligned with business objectives. In this role, you will: Transform complex business challenges into data-driven intelligence by identifying high-value problems, framing them analytically, and engineering robust features and insights. Design, develop, train, and implement mathematical models, algorithms, and machine learning solutions to solve complex business problems. Apply advanced techniques across exploratory analysis, statistical modeling, experimentation, forecasting, segmentation, anomaly detection, causal analysis, and machine learning. Integrate analytical and machine learning solutions into products, prototypes, or business processes, ensuring enterprise-grade scalability and robustness. Define success metrics, quantify business impact, and translate analytical results into clear, actionable recommendations for decision-makers. Lead or manage complex analytical workstreams or projects, including deliverables, milestones, risks, and escalations, as appropriate. Communicate insights, trade-offs, and recommendations effectively to cross-functional teams and customer stakeholders, influencing decisions through evidence and expertise. Work in an embedded, customer-facing setup, collaborating closely with customer teams to deeply understand their data, applications, and business context — co-developing impactful data and AI-driven solutions.

Requirements

10 + years of expert-level proficiency in data science and machine learning, with strong command of Python and SQL, and the ability to design, validate, and optimize end-to-end analytical and ML solutions.
Hands-on expertise with Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
Deep applied knowledge of statistics and experimentation, including hypothesis testing, experimental design, causal inference, and uncertainty interpretation.
Proven experience applying a wide range of ML techniques — classification, forecasting, clustering, recommendation systems, anomaly detection — balancing accuracy, robustness, interpretability, and usability at scale.
Strong capability to translate ambiguous business questions into measurable analytical problems and connect model outputs to commercial, operational, or strategic decisions.
Experience delivering production-ready analytics and ML systems on modern data platforms, with a strong focus on data quality, governance, and reusability.
Exposure to SAP data landscapes including SAP S/4HANA, SAP BW/4HANA, SAP HANA Cloud, and familiarity with SAP Business Technology Platform (BTP), AI Core, AI Launchpad, and SAP AI services.
Ability to operate effectively in an Agentic AI context, contributing to the design, application, and responsible use of AI agents within complex business processes.
Advanced communication and collaboration skills, with the ability to influence senior stakeholders through clear data narratives and executive-ready insights.
You proactively leverage AI in everyday workflows, ensuring high-quality outputs through thoughtful context design and system integration.

Nice To Haves

Exposure to SAP data landscapes including SAP S/4HANA, SAP BW/4HANA, SAP HANA Cloud, and familiarity with SAP Business Technology Platform (BTP), AI Core, AI Launchpad, and SAP AI services.
Ability to operate effectively in an Agentic AI context, contributing to the design, application, and responsible use of AI agents within complex business processes.

Responsibilities

Transform complex business challenges into data-driven intelligence by identifying high-value problems, framing them analytically, and engineering robust features and insights.
Design, develop, train, and implement mathematical models, algorithms, and machine learning solutions to solve complex business problems.
Apply advanced techniques across exploratory analysis, statistical modeling, experimentation, forecasting, segmentation, anomaly detection, causal analysis, and machine learning.
Integrate analytical and machine learning solutions into products, prototypes, or business processes, ensuring enterprise-grade scalability and robustness.
Define success metrics, quantify business impact, and translate analytical results into clear, actionable recommendations for decision-makers.
Lead or manage complex analytical workstreams or projects, including deliverables, milestones, risks, and escalations, as appropriate.
Communicate insights, trade-offs, and recommendations effectively to cross-functional teams and customer stakeholders, influencing decisions through evidence and expertise.
Work in an embedded, customer-facing setup, collaborating closely with customer teams to deeply understand their data, applications, and business context — co-developing impactful data and AI-driven solutions.