Data Science (Remote)

# Data Science Lead ## About the Role Prove is looking for a Data Science Lead to serve as a strategic architect and research leader for its data ecosystem. This role focuses on designing scalable data architectures, leading R&D for novel data sources, establishing rigorous analytical methodologies, and ensuring high-quality data and analytical solutions can be integrated into production systems. The Data Science Lead will work across data science, data engineering, machine learning, analytics, and production integration. The role requires strong technical leadership, research capabilities, and the ability to translate complex data architectures and methodologies into strategic business value. Department: Business Operations Reports To: Director, Data Science FLSA Status: Exempt Location: US Remote Work Authorization: This role is not eligible for work authorization sponsorship. ## Key Responsibilities ### Data Architecture & Scalable Engineering Design and oversee the evolution of scalable data architectures supporting advanced analytics, machine learning modeling, and real-time processing. Develop data architecture approaches that support current and future data science requirements. ### R&D & Novel Data Source Evaluation Research, evaluate, and pressure-test internal, external, and alternative data sources, including synthetic data, IoT streams, and third-party APIs. Assess the predictive power and commercial viability of new data sources. Lead ideation and feature engineering for novel data sources and document alignment with current and future data architecture. Lead rapid prototyping and proof-of-concept initiatives to validate new technologies, algorithms, and data structures before production scaling. Technically evaluate data vendors and partners for data quality, density, and integration capabilities. ### Methodology & Analytical Rigor Define and document standard methodologies for statistical analysis, experimental design, including A/B testing, and machine learning modeling. Establish validation protocols and evaluation metrics such as precision/recall, drift detection, and bias/fairness audits. Promote data and model integrity through rigorous evaluation practices. Translate academic research and emerging industry trends into practical data science methodologies. ### Ingestion & Solution Integration Bridge R&D and production by ensuring analytical models and new data sources can be integrated into core business products and solutions. Oversee data delivery contracts between the data science ecosystem and downstream software applications. Guide the creation of clean, well-documented APIs for data delivery and integration. ## Key Deliverables — First 12 Months Data Source Playbook: Develop a framework for scoring, vetting, and onboarding new data assets. Methodology Registry: Establish a centralized repository of approved statistical models, evaluation metrics, and ingestion protocols. Feature Importance Registry & Feature Engineering Roadmap: Create a centralized repository connecting current data sources to product value, the impact of removal or potential substitutes, and opportunities to leverage signals in new and differentiated ways. Architectural Roadmap: Develop a 12-month to 3-year vision aligning data science infrastructure with corporate scaling goals. ## Required Qualifications 6+ years of experience in Data Science or Data Engineering. At least 2 years of experience in a technical leadership or architectural role. Proficiency with Python, R, and SQL. Experience with cloud platforms such as AWS, GCP, or Azure. Experience with big data technologies such as Spark and Kafka. Experience with orchestration tools such as Airflow. Experience with MLOps tools and deployment patterns. Deep understanding of data modeling and schema design across SQL and NoSQL environments. Strong understanding of statistical evaluation and MLOps deployment patterns, particularly in R&D environments that bridge research and production. Exceptional ability to translate complex technical architectures into strategic business value for non-technical stakeholders. ## Preferred Qualifications The source description does not identify additional qualifications as preferred. The qualifications listed above represent the stated profile requirements. ## Skills & Competencies Data science and data engineering Data architecture Data modeling and schema design Statistical analysis and evaluation Experimental design and A/B testing Machine learning MLOps Big data technologies Cloud platforms Data ingestion and integration API and interface design Feature engineering Research and development Proof-of-concept development Technical leadership Strategic problem-solving Business communication and stakeholder translation ## Education & Experience Education: The source job description does not specify a required degree or educational credential. Experience: 6+ years in Data Science/Data Engineering. 2+ years in a technical leadership or architectural role. ## Work Arrangement & Schedule Employment type: Full-time Work arrangement: Remote Location: United States FLSA status: Exempt This position is not eligible for work authorization sponsorship. Responsibilities may evolve based on the successful candidate’s capabilities and changing business conditions. The role may include other related duties as assigned by the supervisor. ## Compensation & Benefits Anticipated Salary Range: $179,000–$200,000 USD, plus variable commission/company bonus. Offered compensation will be determined based on factors including education, experience, knowledge, skills, geographic location, abilities, internal equity, and market data. Prove follows a market-driven compensation philosophy based on geographic location and applicable market rates. Job offers are aligned to location. Geographic Compensation Definitions: Metro 2: NYC metro area, Seattle metro area, Los Angeles metro area, and Miami metro area. Metro 3: All other cities across the domestic United States, except the San Francisco Bay Area. Benefits & Perks for Full-Time Provers: Competitive salaries, bonus plan for eligible roles, and equity plan Modern Health for financial, mental, and physical wellness 401(k) retirement plan and match for US offices Unlimited vacation and flexible hours Comprehensive medical benefits for employees and families Emotional and physical wellness services, including EAP and Prove Well-Being Reimbursement Snacks and beverages at certain office locations Daily GrubHub lunch stipend when coming into US offices

• Collaborative workplace environment

## Compliance / Additional Information Prove is an equal opportunity employer committed to providing equal employment opportunities regardless of race, color, religion, gender, sexual orientation, age, marital status, national origin, citizenship status, disability, veteran status, or other personal characteristics. Prove collects and uses personal information during the job application process. Applicants should refer to Prove’s Recruitment Privacy Policy for information about how personal information is handled and protected. Prove encourages candidates to apply even if their experience does not align perfectly with every qualification listed, as other roles may also be a fit.