Data Scientist

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interVal

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

☁️ SaaS

💳 Fintech

🤖 Artificial Intelligence

💰 $1.2M Seed Round - InterVal on 2021-04

SaaS • Fintech • Artificial Intelligence

interVal is a SaaS platform that uses machine learning and natural language processing to extract, analyze, and surface actionable insights from year-end financial and tax documents. It transforms raw financial statements and integrated bookkeeping data (QuickBooks Online, Xero, Sage, Caseware) into valuation metrics, business health KPIs, protection gaps, loan and investment signals, and client-ready reports to help wealth managers, accounting firms, banks, and other financial institutions identify opportunities and grow AUM. The platform emphasizes enterprise-grade security (SOC 2 Type II, AWS hosting), automated advisory workflows, and features aimed at making advisors more proactive and efficient when serving SMB clients.

📋 Description

• Design, build, and deploy AI/ML models for diverse business applications, emphasizing privacy and compliance. • Partner with data engineers and AI/ML engineers to develop robust, trustworthy 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 meaningful insights and identify actionable opportunities. • Collaborate with stakeholders to scope projects and communicate key findings. • Monitor, evaluate, and improve the performance and explainability of AI solutions. • 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 a related field. • Demonstrated experience with machine learning, statistical modeling, and analytics. • Proficiency in Python (or similar language), SQL, and relevant ML/data science libraries. • Strong knowledge of privacy-preserving techniques (federated learning, differential privacy) or interest in privacy-first AI/ML development. • Knowledge of data engineering/big data tools (e.g., Spark, Airflow, Kafka) is beneficial. • Experience working on cloud, hybrid, and/or on-premise infrastructure. • Familiarity with regulatory environments (GDPR, CCPA) and data sovereignty issues is a plus. • Excellent communication and problem-solving skills; curiosity and adaptability.

🏖️ Benefits

• Impact: Shape a platform that enables enterprises to control, secure, and monetize their most valuable asset—data. • Innovation: Work at the intersection of AI, privacy, and data infrastructure. • Growth: Collaborate with a dynamic team revolutionizing data use across multiple industries.