Data Science Lead

Prove

23h • $179,000 - $200,000 • Remote

About The Position

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.

Requirements

• 6+ years in Data Science/Data Engineering, with at least 2 years in a technical leadership or architectural role.
• Python, R, SQL, Cloud Platforms (AWS/GCP/Azure), Big Data tech (Spark, Kafka), Orchestration (Airflow), and MLOps tools.
• 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.
• Exceptional ability to translate complex technical architectures into strategic business value for non-technical stakeholders.

Responsibilities

• Design and oversee the evolution of scalable data architectures that support advanced analytics, machine learning (ML) modeling, and real-time processing.
• Scout, evaluate, and pressure-test new internal, external, and alternative data sources for predictive power and commercial viability.
• Lead the ideation and feature engineering for new data sources and document how it aligns to current and future data architecture designs.
• Lead rapid prototyping and PoCs to validate new technologies, algorithms, and data structures before scaling them to production.
• Technical vetting of data vendors and partners to ensure data quality, density, and seamless integration capabilities.
• Define and document the organization's gold-standard methodologies for statistical analysis, experimental design (A/B testing), and ML modeling.
• Establish rigorous validation protocols and evaluation metrics to ensure model and data integrity.
• Keep the organization at the cutting edge of data science by translating academic research and emerging industry trends into practical business methodologies.
• 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.
• Oversee data delivery contracts between the DS ecosystem and downstream software applications to ensure the creation of clean, well-documented APIs.

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)
• 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!

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed