Data Scientist

Interval

Completely RemoteFull TimeInformation Technology
Posted Today

Job description

Responsibilities

  • Design, build, and deploy AI/ML models for diverse enterprise applications with a privacy-first approach
  • Partner with data engineers and AI/ML engineers to develop robust data pipelines and ensure reliable model deployment
  • Implement privacy-first analytics techniques supporting data sovereignty and regulatory compliance
  • Scale analyses of large, complex datasets to extract actionable insights
  • Collaborate with stakeholders to scope projects and communicate findings
  • Monitor, evaluate, and improve model performance and explainability
  • Develop and maintain clear documentation, experiment logs, and analytic artifacts

Requirements

  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field
  • Demonstrated experience with machine learning, statistical modeling, and analytics
  • Proficiency in Python, SQL, and relevant ML/data science libraries
  • Strong knowledge of privacy-preserving techniques such as federated learning and differential privacy
  • Familiarity with data engineering and big data tools (e.g., Spark, Airflow, Kafka)
  • Experience working on cloud, hybrid, and/or on-premise infrastructure
  • Familiarity with GDPR, CCPA, and data sovereignty issues preferred
  • Excellent communication, problem-solving skills, curiosity, and adaptability

About the Company

Interval helps enterprises turn messy, underused data into governed, high-confidence intelligence without handing control to a black box. The platform brings compute to your data with a private data lakehouse, verifiable audit trails, and U-AI, a contextual AI framework for secure AI workflows. Interval focuses on three outcomes: control over data ownership, verifiable audit trails, and monetization through private, permissioned data exchange.

Skills & tools

PythonSQLMachine LearningSparkAirflowKafkaCloudData Analysis

What the team is looking for

Use this list as a quick fit check before you apply.

  1. 01Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field
  2. 02Proficiency in Python, SQL, and relevant ML/data science libraries
  3. 03Strong knowledge of privacy-preserving techniques such as federated learning and differential privacy
  4. 04Familiarity with data engineering and big data tools (e.g., Spark, Airflow, Kafka)
  5. 05Experience working on cloud, hybrid, and/or on-premise infrastructure
  6. 06Familiarity with GDPR, CCPA, and data sovereignty issues
  7. 07Excellent communication, problem-solving skills, curiosity, and adaptability
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