Lead Data Scientist

About Prove As the world moves to a mobile-first economy, businesses need to modernize how they acquire, engage with and enable consumers. Prove’s phone-centric identity tokenization and passive cryptographic authentication solutions reduce friction, enhance security and privacy across all digital channels, and accelerate revenues while reducing operating expenses and fraud losses. Over 1,000 enterprise customers use Prove’s platform to process 20 billion customer requests annually across industries, including banking, lending, healthcare, gaming, crypto, e-commerce, marketplaces, and payments. For the latest updates from Prove, follow us on LinkedIn. Prove is driving the future of digital identity. We are looking for Provers who know how to make an impact. We’re talking self-starting professionals who thrive in a fast-paced environment, process information quickly, and make intelligent decisions. The work is challenging and requires not only smart but natural curiosity and tenacity. Teamwork is also important to us – we work together and play together. Prove has big plans, and we’re excited about the future. If this sounds like the place for you – come join our team! Title: Data Science Lead Department: Business Operations Reports To: Director, Data Science FLSA Status: Exempt Location: US Remote This role is not eligible for work authorization sponsorship. Summary: The Data Science Lead will serve as the strategic architect and research pioneer for the organization’s data ecosystem. This role is responsible for designing robust data architectures, leading research and development (R&D) for novel data sources, establishing rigorous analytical methodologies, and ensuring the seamless, scalable ingestion of high-quality data into downstream production solutions. Core Pillars of Responsibility 1. Data Architecture & Scalable Engineering Blueprint Design: Design and oversee the evolution of scalable data architectures that support advanced analytics, machine learning (ML) modeling, and real-time processing. 2. R&D & Novel Data Source Evaluation Exploratory Research: Scout, evaluate, and pressure-test new internal, external, and alternative data sources (e.g., synthetic data, IoT streams, third-party APIs) for predictive power and commercial viability. Lead the ideation and feature engineering for these data sources and document how it aligns to current and future data architecture designs. Proof of Concepts (PoCs): Lead rapid prototyping and PoCs to validate new technologies, algorithms, and data structures before scaling them to production. Vendor & Partner Assessment: Technical vetting of data vendors and partners to ensure data quality, density, and seamless integration capabilities. 3. Methodology & Analytical Rigor Framework Standardization: Define and document the organization's gold-standard methodologies for statistical analysis, experimental design (A/B testing), and ML modeling. Evaluation Metrics: Establish rigorous validation protocols and evaluation metrics (e.g., precision/recall, drift detection, bias/fairness audits) to ensure model and data integrity. Continuous Improvement: Keep the organization at the cutting edge of data science by translating academic research and emerging industry trends into practical business methodologies. 4. Ingestion & Solution Integration Productionalization Bridge: Serve as the critical bridge between R&D and Production, ensuring that complex analytical models and data sources are seamlessly ingested into core business products and solutions. API & Interface Design: Oversee data delivery contracts between the DS ecosystem and downstream software applications to ensure the creation of clean, well-documented APIs. Key Deliverables (First 12 Months) Data Source Playbook: A formalized framework for scoring, vetting, and onboarding new data assets. Methodology Registry: A centralized repository of approved statistical models, evaluation metrics, and ingestion protocols to ensure team-wide consistency. Feature Importance Registry & Feature Engineering Roadmap: a centralized repository connecting current data sources to their product value and impact of removal and/or possible substitutes to the roadmap of how Prove can leverage the signals in new and differentiated ways Architectural Roadmap: A 12 month to 3-year vision aligning data science infrastructure with corporate scaling goals. Profile & Qualifications 6+ years in Data Science/Data Engineering, with at least 2 years in a technical leadership or architectural role. Technical Stack Python, R, SQL, Cloud Platforms (AWS/GCP/Azure), Big Data tech (Spark, Kafka), Orchestration (Airflow), and MLOps tools. Expertise Deep understanding of data modeling, schema design (SQL/NoSQL), statistical evaluation, and MLOps deployment patterns, especially in R&D functions that bridge research with production. Soft Skills Exceptional ability to translate complex technical architectures into strategic business value for non-technical stakeholders. This position description should not be considered the final description of the position. The position description is not intended to be an all-inclusive list of duties and standards of the positions. It should be assumed that we would, to some extent, structure responsibilities in accordance with the successful candidate’s capabilities and changing business conditions. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor. The anticipated salary range for this role is $179,000 - 200,000 plus variable commission / company bonus. Offered salary will be determined by the applicant’s education, experience, knowledge, skills, geo-location and abilities, as well as internal equity and alignment with market data. Prove follows a market driven compensation philosophy based on geographic location and respective market rates. Job offers will be aligned to location. Please speak with your recruiter if you have questions. Prove defines: Metro 2 - NYC metro area, Seattle metro area, Los Angeles metro area, and the Miami metro area. Metro 3 - all other cities across the domestic United States, with the exception of the San Francisco Bay Area. Benefits & Perks for FTE Provers: 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! Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Prove we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Equal Opportunity Employment: Prove is an equal opportunity employer committed to providing equal employment opportunity for all people regardless of race, color, religion, gender or sexual orientation, age, marital status, national origin, citizenship status, disability, veteran status or other personal characteristics Privacy & Data Protection: When you are applying for a job at Prove, we collect and use your personal information in the job application process. To understand more about how Prove uses your personal information, please see our Recruitment Privacy Policy on our website.