Data Science

Prove is seeking a Data Science Lead to serve as a strategic architect and research leader for the organization’s data ecosystem. Reporting to the Director, Data Science within Business Operations, this role will design scalable data architectures, lead research and development for novel data sources, establish rigorous analytical methodologies, and support the scalable integration of high-quality data into production solutions.

The Data Science Lead will bridge research and production by evaluating emerging data sources and technologies, developing analytical and machine learning methodologies, and ensuring data science solutions can be integrated effectively into business products and applications., * Design and oversee scalable data architectures supporting advanced analytics, machine learning modeling, and real-time processing.

• Research and evaluate internal, external, and alternative data sources, including synthetic data, IoT streams, and third-party APIs.

• Lead feature engineering and assess the predictive power and commercial viability of new data sources.

• Document how new data sources align with current and future data architecture designs.

• Lead rapid prototyping and proof-of-concept initiatives to validate new technologies, algorithms, and data structures before production deployment.

• Technically assess data vendors and partners for data quality, density, and integration capabilities.

• Define and document standardized methodologies for statistical analysis, experimental design, A/B testing, and machine learning modeling.

• Establish validation protocols and evaluation metrics, including precision/recall, drift detection, and bias/fairness audits.

• Translate academic research and emerging industry trends into practical data science methodologies.

• Bridge R&D and production to support the integration of analytical models and data sources into core business products and solutions.

• Oversee data delivery contracts between the data science ecosystem and downstream software applications.

• Support the development of clean, well-documented APIs and interfaces for data delivery.

• Develop and maintain frameworks and roadmaps for data source evaluation, methodology standardization, feature engineering, and data science architecture., This position description is not intended to be an all-inclusive list of duties and standards. Responsibilities may be structured according to the successful candidate’s capabilities and changing business conditions, and additional related duties may be assigned by the supervisor.

• 6+ years of experience in Data Science and/or Data Engineering.

• At least 2 years of experience in a technical leadership or architectural role.

• Strong proficiency in:

• Python

• R

• SQL

• Experience with cloud platforms such as AWS, GCP, or Azure.

• Experience with big data technologies such as Spark and Kafka.

• Experience with orchestration technologies such as Airflow.

• Experience with MLOps tools and deployment patterns.

• Deep understanding of data modeling and schema design, including SQL and NoSQL.

• Strong knowledge of statistical evaluation and machine learning methodologies.

• Experience bridging research and development with production data science solutions.

• Ability to translate complex technical architectures into strategic business value for non-technical stakeholders.

Preferred Qualifications

The source description does not specify additional preferred qualifications. Candidates are encouraged to apply even if their experience does not align perfectly with every qualification listed.

Skills & Competencies

• Data Science

• Data Engineering

• Data Architecture

• Machine Learning

• Statistical Analysis

• Experimental Design and A/B Testing

• Feature Engineering

• Data Modeling

• SQL and NoSQL Schema Design

• MLOps

• Big Data

• Cloud Platforms

• Research & Development

• Proofs of Concept and Rapid Prototyping

• Data Source Evaluation

• API and Data Interface Design

• Technical Leadership

• Strategic Communication

• Analytical Rigor

Education & Experience

Education:

No specific educational degree or field is stated in the source description.

Experience:

• 6+ years in Data Science and/or Data Engineering.

• 2+ years in a technical leadership or architectural role.

Work Arrangement & Schedule

Anticipated Salary: $179,000-$200,000 plus variable commission/company bonus.

Offered salary is 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.

Benefits & Perks for FTE Provers include:

• Competitive salaries and bonus plan for eligible roles

• Equity plan

• Modern Health financial, mental, and physical wellness support

• 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

• Additional office-specific perks, including snacks, beverages, and GrubHub lunch stipend for eligible US office locations

Key Deliverables - First 12 Months

• Data Source Playbook: Framework for scoring, vetting, and onboarding new data assets.

• Methodology Registry: Centralized repository of approved statistical models, evaluation metrics, and ingestion protocols.

• Feature Importance Registry & Feature Engineering Roadmap: Repository connecting current data sources to product value, removal impact, potential substitutes, and opportunities to leverage differentiated signals.

• Architectural Roadmap: 12-month to 3-year vision aligning data science infrastructure with corporate scaling goals.

Prove provides phone-centric identity tokenization and passive cryptographic authentication solutions designed to reduce friction, enhance security and privacy, accelerate revenue, and reduce fraud losses across digital channels. Its platform serves enterprise customers across industries including banking, lending, healthcare, gaming, crypto, e-commerce, marketplaces, and payments.