Data Scientist at Prove

Job description

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 Scientist Department: Data Engineering - Engineering Reports To: Director, Data Science FLSA Status: Exempt Location: United States (Remote) Please note we are hiring multiple Data Scientists at different levels in the organization. Level and compensation will be based on individual qualifications and interview performance. This role is not eligible for work authorization sponsorship. Job Summary We are seeking a Senior Data Scientist who combines strong expertise in statistical analysis and applied machine learning with practical experience in production-oriented workflows. This role is not just about building models in isolation—you will partner closely with Senior Machine Learning Engineers to ensure models are production-ready, monitored, and continuously improved. You’ll focus on generating insights, developing models, and proactively collaborating in monitoring deployed algorithms. Beyond the technical, you’ll help identify opportunities for model-driven enhancements and business and customer recommendations based on real-world performance. Key Responsibilities Statistical & ML Modeling Develop, validate, and tune statistical and machine learning models that solve complex business problems. Partner with engineers to ensure models are designed with production deployment in mind. Design experiments and evaluate models using robust statistical methodologies. Build and maintain dashboards that proactively monitor product efficacy and distribute key insights across product and customer domains. Production Awareness & Monitoring Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure. Proactively monitor deployed models for drift, accuracy, and reliability. Provide insights and business recommendations based on model performance in production. Recommend retraining or refinement strategies in response to performance changes. Business Impact & Strategy Translate model results into actionable recommendations for both product and business teams. Identify opportunities for model improvements that drive up-sell, revenue growth, and cost reduction. Communicate results clearly to both technical and non-technical stakeholders. Collaboration & Leadership Work side-by-side with Senior Machine Learning Engineers to ensure smooth handoff from research to deployment. Mentor team in best practices for applied ML and production readiness. Contribute to evolving data science standards and playbooks that prioritize operational impact. Qualifications Education & Experience 5+ years of experience applying machine learning and statistics to business problems. Master’s or PhD in Statistics, Computer Science, Data Science, or related field (or equivalent experience). Prior exposure to production ML environments and workflows. Technical Skills Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow). Strong background in statistical methods (supervised/unsupervised learning, classification models, etc.), experimental design, and data visualization (Looker) and storytelling. Strong proficiency in SQL (Snowflake) with an ability to work with raw data and collaborate with data engineering teams to optimize data pipelines. Proficiency with cloud platforms (AWS). Solid understanding of APIs, model deployment processes, and monitoring practices. Familiarity with other programming languages such as R, Java, and Go. Soft Skills Excellent communication skills with ability to bridge technical and business contexts. Strong problem-solving and proactive ownership mindset. Comfort working in cross-functional teams with engineers, product managers, and business leaders. Ability to balance quick iterations with building long-term scalable solutions. What We Offer Competitive compensation and benefits. A high-impact environment and data-driven culture where your models make it into production and drive measurable business outcomes. Opportunities to shape both data science practices and production ML systems. 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 base salary range for these roles is categorized by level: Data Scientist: $125,000 – $140,000 Senior Data Scientist: $150,000 – $170,000 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.

Requirements

Must-haves

Statistical & ML Modeling
Develop, validate, and tune statistical and machine learning models that solve complex business problems.
Partner with engineers to ensure models are designed with production deployment in mind.
Design experiments and evaluate models using robust statistical methodologies.
Build and maintain dashboards that proactively monitor product efficacy and distribute key insights across product and customer domains.
Production Awareness & Monitoring
Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure.
Proactively monitor deployed models for drift, accuracy, and reliability.
Provide insights and business recommendations based on model performance in production.
Recommend retraining or refinement strategies in response to performance changes.
Business Impact & Strategy
Translate model results into actionable recommendations for both product and business teams.
Identify opportunities for model improvements that drive up-sell, revenue growth, and cost reduction.
Communicate results clearly to both technical and non-technical stakeholders.
Collaboration & Leadership
Work side-by-side with Senior Machine Learning Engineers to ensure smooth handoff from research to deployment.
Mentor team in best practices for applied ML and production readiness.
Contribute to evolving data science standards and playbooks that prioritize operational impact.
Education & Experience
5+ years of experience applying machine learning and statistics to business problems.
Master’s or PhD in Statistics, Computer Science, Data Science, or related field (or equivalent experience).
Prior exposure to production ML environments and workflows.
Technical Skills

Nice-to-haves

Competitive salaries & Bonus Plan (for eligible roles) and Equity Plan

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

401(K)

Pto

Wellness