• Analyze metrics to continually improve core and adjacent products • Contribute to business operations, such as costing, production metrics, planning, and budgeting for new product initiatives • Work cross-functionally to improve the analysis and presentation of products • Monitor the market, competitor activities, any price movements, and make recommendations that will be used in key decision-making • Liaise with other departments to enhance and increase efficiency of effecting price changes in accordance with market shifts • Drive consumer intelligence through development of external and internal data sources that improve business’s understanding of product’s market, competitor activities, and consumer activities • Measure and monitor product or service’s performance, and present consumer, market, and competitive intelligence for product growth • Develop or adopt research tools, sources, and methods that further support and contribute to product marketing management intelligence • Formulate ways to package and market products in a compelling manner • Perform initial product analysis to assess need for requested changes and their potential impact • Support product team in product decisions using quantitative data

• Partner with key stakeholders, including the business unit leaders, Product, Data & Technology teams, to assess their data and analytics needs by eliciting data requirements, and researching internal systems of record and publicly available data sources and assessing their quality of data. • Requisition and obtain private data sources to support business needs for data and insights. • Identify translation or transformation requirements for sourced data into the data warehouse. • Improve quality of sourced data by data curation and validation rules as well as maintaining the Master Data tables. • Maintain the Logical Data Dictionary (LDD) with data lineage information (field definition, domain, source system, intermediary and destination system(s), translations, transformation rules, display screens, referenced reports, etc.), Data Stewards and Data Custodian info along with versioning for historic analysis and audit trail. • Ensure consistent delivery of high-quality insights to key stakeholders by reconciling and validating dashboards and reports in a timely manner. • Document functional / non-functional business requirements for collecting, loading, transforming, curating, processing data and for visualization and distribution of insights. • Identify internal process improvements for automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. • Provide actionable insights using analytics tools into customer acquisition, operational efficiency and other key business performance metrics. • Identify Personally Identifiable Information (PII) data in primary data sources as well as in data architecture, define masking and other data processing rules for special handling of PII data as well as maintain access control lists for visibility of PII data on a need-to-know basis. • Identify and document the Business Continuity Plan/Disaster Recovery (BCP/DR), and technical requirements for high availability and recoverability for the data architecture. • Improve the quality of data used for analysis by assessing the accuracy of new data sources and the effectiveness of new data gathering techniques. • Define Key Performance Indicators (KPIs) and document technical processes to monitor and analyze data warehouse performance and data accuracy. • Contribute to the team of data professionals by using expertise to answer questions and sharing templates, reusable artifacts with less experienced teammates, enhancing skillsets and competencies of team members, and sharing business domain knowledge throughout the team. • Participate in stakeholder reviews and design sessions. • Provide data, reports, and information to management as needed. • Identify, track, and monitor trends and avoidable technology-related errors; work across functions to develop complex solutions, improvements, and stop-gaps. • Focus on the continual improvement of policies, procedures, and processes falling under scope of authority. • Use expertise to resolve high level issues that cannot be solved by teammates. • Stay abreast of latest technology trends and participate in high-level decisions impacting the direction of Information Technology function. • Consistently monitor and model platform usage, database sizes, compute resources and third- party costs to ensure that the data team’s spend is as cost-efficient as possible. • Champion data transformation to new ways of working and generating insights. • Drive strong communications, partnerships, and stakeholder management with senior leaders, functional managers and staff. • Perform other duties as assigned.

We are seeking an On-Chain Data Analyst to strengthen the firm’s on-chain research capabilities by internalizing and systematizing critical data and attribution workflows. This role is central to improving the quality, reliability, and scalability of on-chain research used to inform market analysis and investment decision-making. The ideal candidate is an experienced on-chain engineer or data professional who is comfortable operating close to research. You will work in a highly dynamic, market-driven environment, translating ambiguous research questions into research‑grade datasets, improving entity‑level attribution, and helping evolve ad‑hoc analysis into durable systems. Responsibilities - Build and maintain EVM-chain attribution and labeling datasets, including address labeling (exchanges, whales, protocols, DAOs), entity clustering, and QA to ensure research-grade data quality. - Translate research questions into reproducible datasets and analytical workflows, including token supply/distribution tracking, exchange flow analysis, and entity-level behavioral views that researchers rely on for ongoing analysis. - Systematize coverage of token launches, airdrops, and distribution events by creating reusable templates and frameworks that reduce ad-hoc coverage effort. - Support ownership transitions of infrastructure components (label database, on-chain monitor) to the Data Platform Team through technical specifications and documentation, acting as a subject-matter bridge between research and engineering teams during handoff phases. - Validate research assumptions, edge cases, and data correctness in analysis outputs and dashboards. - Conduct exploratory analysis using attribution datasets to identify behavioral patterns, reduce noise, and surface potential market-relevant signals.

• Assist with extracting, cleaning, and preparing datasets for analysis. • Contribute to the development of basic reports and data visualizations for internal stakeholders and clients. • Analyze patterns and trends in data, supporting the use of statistical models and machine learning. • Collaborate with business teams to provide actionable insights. • Support the identification of data quality issues and assist in their resolution.