Job Title

Job OverviewWe are looking for an experienced Freelance Data Scientist to join a high-performing engineering team working on scalable products for global enterprise clients.This role is ideal for professionals currently working in a 100% REMOTE setup and looking to take up a long-term freelance or part-time engagement alongside their primary role with competitive compensation and meaningful additional income.Company BackgroundWe are a product engineering firm partnering with top-tier global organizations across consulting, fintech, retail, and consumer tech. Our 750+ strong engineering team delivers high-impact solutions across Product and Data Engineering, with deep expertise in Agentic AI, GenAI, RPA, and Full-stack development.Key ResponsibilitiesKnowledge Graphs & Graph-Based SystemsDesign, build, and evolve enterprise knowledge graph systems, including:Ontology and schema designEntity modeling and relationship extractionGraph alignment, merging, versioning, and schema evolutionOperate knowledge graphs across heterogeneous enterprise data sources at scaleEnterprise Search & RetrievalDevelop and operate enterprise search and information retrieval systems using hybrid approaches, combining:Semantic retrieval (embeddings, vectors)Graph traversal and reasoningSymbolic search and metadata-based retrievalLearning-to-rank for high-precision enterprise use casesGraph-Based Reasoning & ExplainabilityImplement graph-based reasoning and memory systems to support:Multi-hop reasoningContext persistenceExplainable and traceable decision pathsApply graph algorithms such as ranking, path finding, entity resolution, link prediction, and community detection to improve relevance, coverage, and inference qualityLLM, RAG & Agentic AI SystemsBuild graph-augmented RAG pipelines integrating knowledge graphs with LLMsDevelop agentic AI systems supporting planning, tool use, retrieval, grounding, and long-horizon task executionImplement MCP-based workflows and orchestration for multi-step enterprise AI use casesMachine Learning & NLPDesign and scale traditional ML and NLP systems, including:Recommender systemsLearning-to-rank modelsTopic modelingEntity resolution and classification pipelinesOwn ML evaluation and benchmarking, including offline metrics, error analysis, and online experimentationCollaboration & Platform IntegrationWork closely with data engineering and platform teams on large-scale pipelines, vector databases, feature stores, and graph databasesPartner with product, security, and compliance teams to integrate AI capabilities into Simpplr’s enterprise platformEngagement DetailsCommitment: ~35 hours per week with predictable wokload and clear deliverablesWork Model: 100% remote engagement from our sideCompensation: Monthly payouts with a transparent pay cycleTenure: Long-term engagement for high performers; continuity and stability assuredEligibility: Open only to professionals currently working in a full-time remote role (validated during screening)Growth: Opportunity to work on complex, high-impact products alongside senior engineering leadership