Data Science (WFH)

Yes — I can complete this one. The text you pasted is truncated after “Benefits & Perks for FTE Provers:”, so I’ll preserve everything provided and complete the JD structure without inventing specific benefits that aren’t included in the source. ## About the Role Prove is seeking a Data Science Lead to serve as a strategic architect and research leader for the organization’s data ecosystem. This role will focus on designing scalable data architectures, evaluating and developing novel data sources, establishing rigorous analytical methodologies, and connecting research and development efforts with production data solutions. The Data Science Lead will work across data science, data engineering, machine learning, and business operations to develop scalable infrastructure, validate new technologies and data sources, establish analytical standards, and ensure high-quality data is integrated into downstream products and solutions. The role reports to the Director, Data Science and is fully remote within the United States. ## Key Responsibilities ### Data Architecture & Scalable Engineering Design and oversee scalable data architectures supporting advanced analytics, machine learning models, and real-time processing. Develop architectural approaches that support both current analytical requirements and future organizational growth. Align data science infrastructure with long-term business and technology objectives. ### R&D & Novel Data Source Evaluation Research, evaluate, and pressure-test new internal, external, and alternative data sources. Assess sources such as synthetic data, IoT streams, and third-party APIs for predictive value and commercial viability. Lead ideation and feature engineering associated with new data sources. Document how new data sources and features align with current and future data architecture. Lead rapid prototypes and proof-of-concepts (PoCs) to validate new technologies, algorithms, and data structures before production deployment. Technically evaluate data vendors and external partners for data quality, density, and integration capabilities. ### Methodology & Analytical Rigor Define and document standardized methodologies for statistical analysis, experimental design, A/B testing, and machine learning modeling. Establish rigorous validation protocols and evaluation metrics for data and model quality. Apply evaluation approaches including precision and recall, drift detection, and bias and fairness audits where appropriate. Monitor emerging data science research and industry trends and translate relevant developments into practical business methodologies. Promote continuous improvement of analytical and data science practices across the organization. ### Ingestion & Solution Integration Act as a bridge between research and development and production teams. Ensure complex analytical models and newly evaluated data sources can be integrated into core business products and solutions. Oversee data delivery contracts between the data science ecosystem and downstream software applications. Support the creation of clean, well-documented APIs and data interfaces. Help ensure data and analytical solutions are scalable, reliable, and production-ready. ## Key Deliverables During the first 12 months, the Data Science Lead will be responsible for developing key organizational frameworks and roadmaps, including: Data Source Playbook: A framework for scoring, evaluating, vetting, and onboarding new data assets. Methodology Registry: A centralized repository of approved statistical models, evaluation metrics, and ingestion protocols to support consistency across teams. Feature Importance Registry & Feature Engineering Roadmap: A centralized framework connecting current data sources and features to product value, assessing the impact of removing features or using substitutes, and identifying opportunities to leverage signals in new and differentiated ways. Architectural Roadmap: A 12-month to 3-year vision aligning data science infrastructure with Prove’s organizational scaling objectives. ## Required Qualifications 6+ years of experience in Data Science, Data Engineering, or a related field. At least 2 years of experience in a technical leadership or architectural role. Strong proficiency with Python. Experience with R. Advanced SQL skills. Experience working with cloud platforms such as AWS, GCP, or Azure. Experience with big data technologies such as Spark and Kafka. Experience with workflow orchestration tools such as Airflow. Experience with MLOps tools and production machine learning workflows. Deep understanding of data modeling and schema design. Experience with both SQL and NoSQL data architectures. Strong understanding of statistical evaluation methodologies. Knowledge of MLOps deployment patterns. Experience bridging research and development initiatives with production data and software solutions. Ability to translate complex technical architectures and analytical concepts into strategic business value for non-technical stakeholders. Strong analytical, research, problem-solving, and communication skills. ## Technical Skills Python R SQL AWS GCP Azure Spark Kafka Airflow MLOps Data modeling SQL and NoSQL schema design Statistical evaluation Machine learning Data architecture API and data interface design ## Work Environment Department: Business Operations Reports To: Director, Data Science Location: US Remote Employment Status: Full-time FLSA Status: Exempt Work Authorization: This role is not eligible for work authorization sponsorship. ## Compensation Anticipated Salary Range: $179,000–$200,000 USD Additional variable commission and/or company bonus may be available. Actual compensation may be determined based on 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 the employee’s location. Prove defines its geographic compensation markets as follows: Metro 2: New York City metro area, Seattle metro area, Los Angeles metro area, and Miami metro area. Metro 3: All other cities across the United States, excluding the San Francisco Bay Area. ## Benefits & Perks Prove provides benefits and perks for full-time employees. Specific benefits and eligibility details are determined according to applicable company plans and employment terms. ## Additional Information This position description is not intended to be an exhaustive list of duties, responsibilities, or performance standards. Responsibilities may be structured according to the successful candidate’s capabilities and changing business conditions. The employee may also be expected to perform other related duties as assigned by their supervisor. Prove is seeking professionals who are curious, analytical, collaborative, and capable of connecting advanced data science and engineering concepts with practical business outcomes.