Lead Data Analyst

Hinge Health
Benagaluru-HQPosted 9 March 2026

Tech Stack

Job Description

Lead Data Analyst Beware of Phishing Attempts: We've noticed an increase in phishing where fraudsters impersonate employees and send fake job offers to steal sensitive information. We'll never ask for financial details during the hiring process and only use "@hingehealth.com http://hingehealth.com" emails. If you receive a suspicious offer, stop communication and report it to the US FBI Internet Crime Complaint Center. To verify an email from our recruiting team, forward it to security@hingehealth.com. The Opportunity Join the Operations Analytics team at Hinge Health and help build the engine that powers our clinical operations. In this role, you will be a highly visible, trusted partner for our product and operations leaders, working on problems that haven’t been solved before at this scale in the digital health space. You will support stakeholders ranging from product managers and clinical leadership to workforce management and business operations, shaping product features and driving operational process improvements through deep-dive analyses. What You’ll Accomplish - Measure Product Performance: Partner with product and engineering to measure product performance, perform feature experimentation, and conduct deep dives to inform roadmapping. - Drive Analytics Initiatives: Lead analytics initiatives across internal product, member experience, clinical outcomes, and business metrics. - Uncover Key Drivers: Conduct root cause analyses and deep dives to determine the underlying drivers of member behaviors and clinical outcomes. - Improve Instrumentation: Work across functions to improve instrumentation and measurement of metrics to better understand the business and highlight key opportunity areas. - Advance Team Maturity: Advance the maturity of the team and organization through continued data modeling, development of best practices and frameworks, and leveraging AI capabilities for stakeholder self-service. Who You Are - Lead at All Levels: You can lead projects with large scope and complexity, taking ownership of outcomes and delivering measurable business impact. - Hustle: You are an expert at navigating vague and ambiguous problem statements, creatively finding ways to solve complex challenges with minimal guidance. - Cross-Functional Partner: You deeply understand operations, product, and engineering processes, allowing you to effectively partner with and influence VP/Director-level stakeholders. Basic Qualifications - 4+ years of experience as a data analyst, product analyst, analytics engineer, or data scientist. - SQL mastery, Python, and data modeling experience. - Strong data visualization and communication skills with the ability to synthesize and tailor complex insights to VP/director-level stakeholders. - Can conduct A/B experimentation with correct methodology, setup [power math, list pulling, dashboard creation], and results interpretation. - Can apply appropriate mathematical and statistical concepts to bolster analyses (averages, distributions, confidence intervals, etc.). - A high attention to detail and the ability to QA data at a glance or in depth. - Ability to work autonomously and with minimal guidance. - Bachelor’s Degree in Computer Science or related technical degree. About Hinge Health At Hinge Health, we’re using technology to scale and automate the delivery of healthcare – starting with musculoskeletal (MSK) conditions, which affect over 1.7 billion people worldwide. With an AI-powered human-centered care model, Hinge Health leverages cutting-edge technology to improve outcomes, experiences and costs to help people move beyond their pain. The platform addresses a broad spectrum of MSK care – from acute injury, to chronic pain, to post-surgical rehabilitation – through personalized, evidence-based care. As the preferred partner to 50+ health plans, PBMs and other ecosystem partners, Hinge Health is available to over 20 million people across more than 2,550 employers. The co ... (truncated, view full listing at source)
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