Senior Data Engineer
Reporting to: Director of Engineering
We are seeking a highly experienced and strategically-minded Senior Data Scientist to lead the development of our core data intelligence assets. This role is central to unlocking complex business value by designing and implementing sophisticated Knowledge Graphs and Data Ontologies while simultaneously driving optimization through cutting-edge Dynamic Pricing Engines. You will bridge advanced semantic modeling with economic theory, ensuring data representations are robust, scalable, and directly feed high-impact predictive systems. You will act as the technical subject matter expert, defining the long-term roadmap and mentoring the next generation of data scientists.
Key Responsibilities
Architectural Strategy & Leadership (40%)
Strategic Design: Define the architectural strategy, vision, and long-term roadmap for enterprise-wide Knowledge Graph (KG) initiatives, ensuring alignment with product and business goals.
Ontology Ownership: Lead the design, implementation, and governance of robust ontologies (RDF, OWL) and schemas to unify complex, heterogeneous data into the central KG.
Advanced Feature Engineering: Drive the utilization of advanced graph techniques, including Graph Neural Networks (GNNs) and sophisticated entity resolution, to extract novel features for all downstream ML systems.
Scalability & Governance: Own the scalability, performance, and seamless integration of the KG within our core data platform, establishing and enforcing data modeling standards.
Advanced Modeling & Implementation (40%)
Pricing Roadmap: Drive the long-term roadmap for algorithmic pricing, overseeing the design, implementation, and rigorous validation of advanced predictive and causal models (e.g., reinforcement learning, uplift modeling, causal inference).
Production Deployment: Provide hands-on technical leadership during model deployment, focusing on implementing low-latency, high-reliability inference services and effective MLOps practices.
Experimental Design: Design and lead robust A/B testing frameworks and experimental strategies to measure the true causal impact and business efficacy of pricing interventions and model changes.
Cross-functional Translation: Translate complex economic and market hypotheses into production-ready data science solutions that directly impact revenue and margin goals.
Simulations: Build a series of tools to model changes based on consumer trends, brands engagements, and token valuations.
Team Leadership & Best Practices (20%)
Mentorship: Act as the primary technical mentor to junior and mid-level data scientists, providing guidance on rigorous model evaluation, high-quality code delivery, and MLOps principles.
SME & Communication: Act as the subject matter expert for data modeling, semantic technology, and advanced statistical inference, clearly communicating strategic findings and model limitations to executive and product stakeholders.
Research Adoption: Proactively evaluate and drive the adoption of state-of-the-art research in graph ML and algorithmic pricing to ensure continuous innovation.
Required Qualifications
Experience: 5+ years of progressive experience as a Data Scientist, with a demonstrable track record of architecting, leading, and deploying high-impact, production-scale ML solutions.
Semantic Expertise: Deep theoretical and practical expertise in ontologies, RDF, OWL, and graph query languages like SPARQL and Cypher.
Knowledge Graph (KG) Mastery: Hands-on experience designing, building, and maintaining production-scale Knowledge Graphs using graph databases (e.g., Neo4j, JanusGraph, Amazon Neptune).
Coding & ML System Design: Mastery of Python and its data science ecosystem (Scikit-learn, PyTorch/TensorFlow), coupled with proven ability to design scalable, high-reliability ML systems.
Advanced Modeling: Expert proficiency in advanced predictive, causal, or reinforcement learning models applied to problems like pricing, demand forecasting, or resource optimization.
Education: Master’s degree or PhD in Computer Science, Data Science, Statistics, Economics, or a related quantitative field.
Preferred Qualifications
Experience designing and implementing real-time, low-latency machine learning inference services.
Familiarity with distributed computing frameworks (e.g., Spark, Dask) for large-scale data processing.
Experience with cloud platforms (AWS, GCP, or Azure) and advanced MLOps tools (e.g., Docker, Kubernetes, MLflow).
Web3 Knowledge: Familiarity with the fundamentals of decentralized systems, blockchain technology, and tokenomics.
Active involvement in the Knowledge Graph or Pricing Optimization communities, including publications or open-source contributions.
Strong background in economics or econometrics, specifically related to elasticity, optimization, and market dynamics.