Forward Deployed Data Scientist Expert

SAP•Stanford, CA

1d•$198,200 - $420,000•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 to solve complex real world business problems at speed. You work closely with customers to deliver scalable, enterprise grade outcomes that drive measurable business value for customers and SAP. You are accountable for the quality, impact, and delivery of complex analytical initiatives. Acting as a subject‑matter expert, you provide clear guidance, manage ambiguity, and ensure data‑driven solutions are robust, reliable, and aligned with business objectives.

Requirements

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.
Ability to operate effectively in an Agentic AI context, contributing to the design, application, and responsible use of AI agents within complex business processes.
Deep applied knowledge of statistics and experimentation, including hypothesis testing, experimental design, causal inference, and uncertainty interpretation.
Proven experience applying a wide range of machine learning techniques (e.g. 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 using modern data platforms, with a strong focus on data quality, governance, and reusability.
Advanced communication and collaboration skills, with the ability to influence senior stakeholders through clear data narratives and executive‑ready insights.
Strong expertise in data modelling, orchestration, and integration across systems
Proficient in Python and SQL
Experience with big data frameworks (PySpark) and Databricks
Use of data orchestration tools (e.g., Airflow)
Hands-on with SAP HANA and enterprise data platforms
Work with columnar/lakehouse formats (Parquet, Delta Lake)
Implement real-time data streaming (Kafka or equivalent)
You demonstrate a proactive approach to leveraging AI in everyday workflows, ensuring high‑quality outputs through thoughtful context design and system integration.

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 data mining or 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 on‑site or in the customer environment to deeply understand their data, applications, and business context, and to co‑develop and deliver impactful data‑ and AI‑driven solutions.
Build end-to-end data pipelines and ETL/ELT processes
Ensure data quality, governance, and compliance (PII, data ethics)
Apply CI/CD, Docker, and Git in data engineering workflows