Data Scientist PRIMARY SKILLS * Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio SPECIALIZATION * Data Science Advanced: Data Specialist JOB REQUIREMENTS * AI Developer Anaplan Key Responsibilities AI Engineering & Development • Build and deliver internal enterprise AI solutions — including LLM integrations, agentic AI workflows, Gemini Enterprise features, and supporting data pipelines — designed for the scale, security, and reliability requirements of a global workforce. • Develop code in Python following the AI engineering standards, coding practices, and technical guardrails. • Participate in technical design reviews and contribute implementation input during intake, design, and production deployment phases. • Evaluate and prototype new AI tools, frameworks, and platforms, providing input on findings to support technical recommendations. • Contribute to the development and maintenance of the LLM abstraction layer and multi-model integrations. • Support technical debt remediation, system observability, and scalability improvements for Anaplan’s internal AI systems. AI Platform Governance & Enablement • Support the AI intake process by contributing technical assessments of feasibility, build complexity, and integration requirements for employee-facing tools. • Ensure all assigned internal AI initiatives meet engineering, security, compliance, and responsible AI standards before and during development. • Develop AI solutions against clear technical specifications • Build and maintain integrations across core enterprise systems (Salesforce, ServiceNow, Gainsight, Workday) as directed. • Implement human-in-the-loop, explainability, and auditability features as required for decision-impacting AI systems. • Support AI agent lifecycle management including monitoring, feedback loops, and continuous improvement. AI Operations & Workforce Adoption • Contribute to the technical deployment and operationalization of AI agents and solutions, supporting production readiness and stability for Anaplan employees. • Support Gemini Enterprise adoption and participate in AI Friday sessions with technical demonstrations and knowledge sharing. • Help build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics. • Communicate engineering decisions and solution outcomes clearly to technical peers and team leads. Innovation, R&D & Emerging Technology • Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities. • Contribute to proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment. • Maintain and contribute to the AI innovation pipeline — a living backlog of high-potential experiments. Cross-Functional Technical Partnership • Work alongside the Transformation & Financial Flexibility team to support identification and technical validation of AI-driven cost-saving opportunities in Anaplan’s internal operations. • Act as a technical contributor and AI subject matter resource to business units identifying internal AI use cases. • Build collaborative working relationships across Engineering, Security, Compliance, Legal, and HR. Required Qualifications • 5+ years of software or AI/ML engineering experience, with a focus on building and shipping production-grade AI or data-driven applications. • Hands-on experience with the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with exposure to enterprise or workforce deployment contexts. • Experience developing and deploying AI applications in cloud environments, including reliability, observability, and performance considerations. • Proficiency in Python and familiarity with enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling. • Exposure to Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise). • Ability to work effectively within defined architectural standards and del