Senior Data Scientist - R01565738

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