We use essential technologies to keep Swiftcruit secure and running. With your permission, we also use analytics and marketing technologies to understand usage and improve the product. You can change this anytime from Cookie Settings in the footer. Read our Cookie Policy.
Senior Data Scientist - R01565738 at Brillio | Swiftcruit
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
