Interested in this Data Scientist role at Prosper?
Apply Now →Skills & Technologies
About This Role
### Your role in our mission
Prosper is seeking a Data Scientist under the Credit Risk Analytics vertical. You will become a core contributor with machine learning expertise in the credit risk team, delivering results that directly impact business value. This is a unique, hybrid role where you will not only build and deploy industry\-leading predictive models but also play a critical role in shaping our credit risk strategy and business decisions.
### How you’ll make an impact
- Build industry\-leading machine learning models for managing credit and fraud risks. Collaborate closely with engineering to deploy models into a production environment.
- Leverage complex data sources (e.g., credit bureau reports, customer\-supplied information) at scale to develop credit and fraud strategies to improve the credit performance and optimize risk decisions.
- Propose and execute strategic solutions to complex business problems, operating effectively within constraints and aligning with broader company objectives.
- Analyze ad\-hoc portfolio performance at a granular segment level on an ongoing basis. Identify trends and conduct root\-cause analysis to isolate key performance drivers. Communicate findings and recommendations to the Risk Management and broader Prosper community.
- Help the team develop internal tools and workflow solutions to increase data science productivity and operational efficiency.
- Actively monitor credit risk models and strategies in production, extracting actionable insights to significantly impact key business metrics.
- Assess the potential usefulness and validity of new machine learning algorithms and features sourced from diverse, alternative data providers.
- Conduct high\-impact, ad\-hoc analyses supporting risk management, investor services, operations, and corporate development initiatives.
### Skills that will help you thrive
- 2\-3\+ years of work experience in fintech, finance, or another high\-impact field applying statistical and machine learning predictive techniques. Consumer lending experience in unsecured personal loans or credit cards is a strong plus.
- Advanced degree (M.S./Ph.D.) preferably in statistics, computer science, engineering, physical sciences, economics, or a related technical field.
- Expert knowledge of statistical programming languages (e.g., Python) and database languages (e.g., SQL).
- Solid understanding of coding best practices, model documentation, and ML ops principles.
- Strong communication skills with the ability to translate complex technical subject matter into clear, actionable business strategies for cross\-functional partners and senior management.
- Strong ability to collaborate seamlessly with people across various functions (engineering, product, compliance) and build strong relationships.
- Ability to work unsupervised in a fast\-paced environment, effectively prioritizing among parallel technical and strategic projects.
- Ability to innovate within regulatory guidelines with a strong commitment to reproducible research and model governance.
- Self\-motivated, results\-oriented, enthusiastic, and a creative thinker who bridges the gap between data science and business strategy.
### Resources to help you prosper
- A connected experience: We prioritize high\-touch collaboration and flexibility. Whether you are working from our San Francisco or Phoenix offices or joining us as a fully remote team member, we provide the digital\-first tools and intentional culture to keep you synced and supported
- Invested in your future: A competitive salary and a 401(k) with a 5% company match to help you build long\-term financial security
- Holistic well\-being: We provide the resources you need to thrive, from flexible time off and paid parental leave to an annual wellness allowance and comprehensive health coverage
- Professional \& personal growth: Take advantage of a suite of premium perks, including Udemy access, childcare assistance, pet insurance, and a bevy of additional savings through Beneplace
### Interview Process
- Recruiter Call: A brief screening to discuss your experience and initial questions.
- Department Interview: Deeper dive into technical skills and project alignment with the Hiring Manager or team member.
- Team/Virtual Interview: Meet team members for collaborative discussions, problem\-solving, or technical exercises.
- Final Round: Discussion with a department head/executive.
Compensation details: The salary for this position is $129,000 \- $179,000 annually, plus bonus and generous benefits. In determining your salary, we will consider your location, experience, and other job\-related factors.
\#LI\-SK1
\#LI\-Remote
*About Us*
Prosper introduced U.S. consumers to an innovative approach to personal finance as the first peer\-to\-peer lending platform in the country. Since 2005, Prosper has helped over 2 million customers achieve financial well\-being through a comprehensive suite of digital personal finance products.
Prosper’s flagship personal loan\* marketplace continues to offer unique value for both borrowers and investors, while the Prosper® Card¹ provides essential access to credit and flexibility for those managing their financial journey. Guided by its mission to advance financial well\-being, Prosper is dedicated to helping people thrive by meeting people where they are with simple, trusted, and affordable financial solutions. Learn more at www.prosper.com.
We’re on a mission to hire the very best, and we are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere. It is important to us that every hire connects with our vision, mission, and core values. Join a leading fintech company that’s democratizing finance for all!
*Our Values*
- *Diversity expands opportunities*
- *Collaboration creates better solutions*
- *Curiosity fuels our innovation*
- *Integrity defines all our relationships*
- *Excellence leads to longevity*
- *Simplicity guides our user experience*
- *Accountability at all levels drives results*
*www.prosper.com*
*Our Story \& Team* *//* *Our Blog*
*Applicants have rights under Federal Employment Laws.*
*Family \& Medical Leave Act (FMLA)*
*Equal Employment Opportunity (EEO)*
*Employee Polygraph Protection Act (EPPA)*
*California applicants: please* *click here* *to view our California Consumer Privacy Act (“CCPA”) Notice for Applicants, which describes your rights under the CCPA.*
*Illinois applicants: please* *click here* *to view our Artificial Intelligence Notice for Applicants.*
*At Prosper, we're looking for people with passion, integrity, and a hunger to learn. We encourage you to apply even if your experience doesn't precisely match the job description. Your unique skill set and diverse perspective will stand out and set you apart from other candidates. Prosper thrives with people who think outside of the box and aren't afraid to challenge the status quo. We invite you to join us on our mission to advance financial well\-being.*
*Prosper is committed to an inclusive and diverse workplace. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law, including the San Francisco Fair Chance Ordinance. Prosper will consider for employment qualified applicants who are non\-US citizens and will provide green card sponsorship.*
- *All personal loans are made by WebBank.*
*¹The Prosper® Card is an unsecured credit card issued by Coastal Community Bank, Member FDIC, pursuant to license by Mastercard® International.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $129K-$179K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Prosper, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($154K) sits 20% below the category median. Disclosed range: $129K to $179K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Prosper AI Hiring
Prosper has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $179K - $179K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.