Data Scientist

Job not on LinkedIn

🔥 4 minutes ago

🇺🇸 United States – Remote

💵 $160k - $215k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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This company has sponsored H1B visas in the past.

👻 Ghost score 0%

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Prove

WebsiteLinkedInAll Job Openings

201 - 500 employees

🏥 Healthcare

🛡️ Insurance

💼 Consulting

Healthcare • Insurance • Consulting

Prove is the world’s most accurate identity verification and authentication platform, trusted by over 1,000 leading companies to reduce fraud and improve consumer experiences. The company offers a range of digital identity solutions, including Prove Pre-Fill®, Prove Identity®, and Prove Auth®, which streamline the onboarding process, enhance authentication, and provide real-time identity management. With advanced KYC-compliant identity verification and solutions that enable a seamless user experience, Prove is widely used across various industries such as banking, healthcare, and fintech to mitigate fraud and accelerate digital onboarding. Prove ensures bank-grade security, offering frictionless and secure identity verification across the globe.

📋 Description

• Develop, validate, and tune statistical and machine learning models for complex business problems • Partner with engineers to design models for production deployment • Design experiments and evaluate models using robust statistical methodologies • Build and maintain dashboards monitoring product efficacy and distributing insights across product and customer domains • Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure • Monitor deployed models for drift, accuracy, and reliability • Provide business recommendations based on model performance in production • Recommend retraining or refinement strategies in response to performance changes • Translate model results into actionable recommendations for product and business teams • Identify model-improvement opportunities driving up-sell, revenue growth, and cost reduction • Communicate results to technical and non-technical stakeholders • Work with Senior Machine Learning Engineers on research-to-deployment handoff • Mentor the team in applied ML and production-readiness best practices • Contribute to data science standards and playbooks prioritizing operational impact • Perform other related duties as assigned by the supervisor

🎯 Requirements

• 5+ years of experience applying machine learning and statistics to business problems • Master’s or PhD in Statistics, Computer Science, Data Science, or related field (or equivalent experience) • Prior exposure to production ML environments and workflows • Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) • Strong background in statistical methods, experimental design, data visualization (Looker), and storytelling • Strong proficiency in SQL (Snowflake) • Proficiency with cloud platforms (AWS) • Solid understanding of APIs, model deployment processes, and monitoring practices • Familiarity with R, Java, and Go • Excellent communication skills with ability to bridge technical and business contexts • Strong problem-solving and proactive ownership mindset • Comfort working in cross-functional teams with engineers, product managers, and business leaders • Ability to balance quick iterations with building long-term scalable solutions • This role is not eligible for work authorization sponsorship

🏖️ Benefits

• Competitive salaries & Bonus Plan (for eligible roles) and Equity Plan • Modern Health for financial, mental, and physical wellness • 401(k) Retirement Plan & Match (US Offices) and Local Country Pension (International Offices) • Unlimited Vacation and Flexible hours • Comprehensive medical benefits for you and your family • Emotional & Physical Wellness – Access to wellness services (EAP & Prove Well-Being Reimbursement) • Bottomless snacks & beverages for certain office locations • Daily GrubHub stipend for lunch if coming into the office (US Offices) • A great place to work and connect with other talented Provers like yourself! • Competitive compensation and benefits • Opportunities to shape both data science practices and production ML systems

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