Data Scientist - Knowledge Graph

Role Overview: As a Data Scientist specializing in Graph & Analytics at Wissen Technology, you will play a crucial role in leveraging graph-based methods and statistical modeling to understand complex relationships, structures, and patterns within large-scale datasets. Your primary focus will be on designing and implementing graph-based models, conducting network analysis, building scalable data pipelines, and generating actionable insights through statistical modeling. You will collaborate with cross-functional teams to deliver analytical solutions and contribute to maintaining well-documented codebases for long-term maintainability. Key Responsibilities: - Design and implement graph-based models to identify patterns, clusters, communities, and relationships within complex datasets. - Apply network analysis techniques using metrics such as clustering coefficient, degree assortativity, density, Gini index, and small-world index. - Perform multi-hop network traversals and community detection using algorithms such as Louvain partitioning and similar graph clustering approaches. - Build and query graph databases (Neo4j, ArangoDB) using Cypher Query Language to extract structural insights from connected data. - Leverage GPU-accelerated graph libraries (cuGraph) to scale graph computations across large datasets efficiently. - Conduct thorough Exploratory Data Analysis (EDA) on large-scale structured and semi-structured datasets to surface quality issues, distributions, and key features. - Build, optimize, and maintain scalable data pipelines and stored procedures across cloud data platforms such as BigQuery, PostgreSQL, or Hive. - Automate data workflows using orchestration tools such as Apache Airflow or Kubeflow Pipelines. - Apply GPU-accelerated computing (CUDA, CuPy, cuDF) to optimize processing performance on high-volume data workloads. - Ensure data integrity, reproducibility, and documentation across all analytical workflows. - Apply statistical modeling techniques to detect behavioral anomalies, trends, and patterns within datasets. - Use time series analysis to identify temporal patterns and changes in data over time. - Translate analytical findings into clear, actionable insights for both technical and non-technical stakeholders. - Build dashboards and reports using visualization tools (e.g., Trino Superset) to communicate results effectively. - Work closely with product, engineering, and business teams to understand requirements and deliver relevant analytical solutions. - Contribute to internal knowledge sharing through workshops, documentation, and peer reviews. - Maintain well-documented codebases and analytical frameworks for long-term maintainability. Qualification Required: Must-Have - 3+ years of experience in a Data Science, Data Analytics, or Graph Analytics role. - Strong proficiency in Python with hands-on experience using NumPy, Pandas, CuPy, and cuDF. - Practical experience with graph analytics libraries NetworkX, cuGraph, Neo4j, or ArangoDB. - Solid understanding of graph theory concepts: community detection, network metrics, graph traversal, and clustering algorithms. - Proficiency in SQL; experience with BigQuery, PostgreSQL, MS SQL, Hive, or similar databases. - Experience with GPU-based computing using CUDA for performance-critical data tasks. - Strong analytical thinking and ability to work independently on ambiguous, open-ended problems. Good to Have - Experience with Cypher Query Language for querying graph databases (Neo4j / ArangoDB). - Familiarity with graph ML frameworks such as PyTorch Geometric. - Exposure to workflow orchestration tools Apache Airflow or Kubeflow Pipelines. - Knowledge of cloud data tools such as Trino, MinIO, or IBM Datastage. - Basic scripting skills in Bash or C++ for automation or performance tasks. - Experience presenting data insights to senior stakeholders or cross-functional teams. - Research publications, patents, or open-source contributions in graph analytics or data science are a strong plus. Role Overview: As a Data Scientist specializing in Graph & Analytics at Wissen Technology, you will play a crucial role in leveraging graph-based methods and statistical modeling to understand complex relationships, structures, and patterns within large-scale datasets. Your primary focus will be on designing and implementing graph-based models, conducting network analysis, building scalable data pipelines, and generating actionable insights through statistical modeling. You will collaborate with cross-functional teams to deliver analytical solutions and contribute to maintaining well-documented codebases for long-term maintainability. Key Responsibilities: - Design and implement graph-based models to identify patterns, clusters, communities, and relationships within complex datasets. - Apply network analysis techniques using metrics such as clustering coefficient, degree assortativity, density, Gini index, and small-world index. - Perform multi-hop