RnD Senior Data Analyst at Ivix

Tel Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

31 Mar, 26

Salary

0.0

Posted On

31 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Data Operations, Analytics Engineering, SQL, Python, Data Validation, Data Quality, ETL, Data Pipelines, Machine Learning, Data Science, Anomaly Detection, Data Assurance, Cloud Environments, Spark, Airflow

Industry

Business Intelligence Platforms

Description

We’re looking for a brilliant, hands-on, and mission-driven R&D Data Analyst to join our team at IVIX. Our technology helps governments uncover hidden business activity and fight financial crimes by transforming massive volumes of public web data into actionable intelligence. In this role, you will own the data end-to-end — from deeply understanding the business flow, to validating pipelines, to ensuring the accuracy, reliability, and usability of the data powering our product. You’ll work closely with engineering, data science, and product teams to ensure our data is technically sound, business-aligned, and ready for analytical and machine learning use cases. About the company IVIX is the first AI-powered solution designed specifically to address a $20 trillion problem: illuminating the global shadow economy. IVIX leverages Open-Source Intelligence (OSINT) and highly advanced, cutting-edge technologies to reveal illicit business activity around the world, empowering governments in their mission to fight financial crime and close the tax gap. IVIX employs a variety of AI tools (deep neural networks, large language models, and predictive modeling) as well as advanced data analytics to rapidly pinpoint large-scale illicit business activity, so government authorities can combat financial crime in the digital age. Led by security, tech, tax and financial crime experts, and advised by a diverse team of former IRS commissioners, IVIX works with dozens of state and federal governments globally. About the postion Take full ownership of the data across R&D — including data models, data flows, and the processes that power our analytics and product features. Perform in-depth data validation and quality assessments to ensure correctness, consistency, and reliability across pipelines. Develop and maintain frameworks and methodologies for data assurance, anomaly detection, and monitoring. Create, manage, and curate golden datasets, ground truth, and annotated datasets used for data science, ML model training, evaluation, and benchmarking. Work closely with data scientists to ensure datasets align with modeling needs, experimental design, and feature development. Analyze complex datasets to surface insights, edge cases, and discrepancies that impact product decisions or customer outcomes. Document data flows, definitions, validation logic, and labeling guidelines while championing data transparency and accessibility across the organization. Serve as the subject-matter expert for data correctness and completeness. Requirements 5+ years of hands-on experience in data analysis, data operations, analytics engineering, or a similar data-focused role. Strong analytical skills and high proficiency in SQL and Python for data investigation, validation, and dataset creation. Experience building and maintaining golden datasets, creating ground truth, and managing annotation/labeling processes for ML or analytics use cases. Familiarity with modern data pipelines, ETL/ELT processes, and data workflow tools. Ability to understand complex business processes and translate them into data logic and validation frameworks. Experience working with large datasets in cloud environments (AWS preferred). Familiarity with tools such as Spark, Airflow, or similar technologies - a strong advantage. A passion for data, problem-solving, and getting into the details.

Responsibilities

The R&D Senior Data Analyst will take full ownership of data across R&D, ensuring its accuracy and reliability while collaborating with various teams. Responsibilities include data validation, creating datasets for machine learning, and documenting data processes.