Senior Data Scientist (Marketing)

Enova; Chicago (IL); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

PTO & paid holidays

Sabbatical program

Summer hours

Paid parental leave

DEI groups

401(k) matching

Job summary

Enova seeks a Senior Data Scientist to own value-based bidding across digital marketing channels. This role sits at the intersection of machine learning and paid media, where your models will drive daily spend and profitability.

You will build lifetime value models, deploy them to ad platforms, run experiments, and collaborate with Marketing and the Pricing & Profitability team to align signals with Enova’s strategies.

Qualifications

• 4+ years of experience in Data Science, Analytics, or a related field.
• Experience deploying models into production systems or building automated data pipelines.
• Exposure to digital marketing or martech/adtech: paid search or paid social platforms, conversion tracking, attribution, or marketing measurement.

Responsibilities

• Own value-based bidding across digital marketing channels and build models that inform real-time bidding decisions.
• Design and implement automated systems that pass value estimates to ad platforms (offline conversions, APIs, server-side integrations).
• Partner with Marketing and Pricing & Profitability to ensure lifetime value signals align with broader profitability models.
• Design and analyze experiments to measure the impact of value-based bidding on acquisition efficiency and portfolio quality.
• Communicate insights and recommendations to Marketing and Data Science leadership.

Skills

Python

SQL

Statistical modeling

Machine learning

Data analysis

Problem-solving

Independent work

Education

Degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related field

Tools

Relational databases

Production deployment

Job description

• We are looking for a Senior Data Scientist to own value-based bidding (VBB) across our digital marketing channels. This is a rare role that sits at the intersection of machine learning and paid media, where your models will be directly making the decisions that drive the digital marketing spend day-to-day
• Here’s the problem you’ll own: platforms like Google Ads and Meta will optimize toward whatever signal we give them. Optimize toward clicks or raw conversions, and we acquire the wrong customers efficiently
• The alternative is to predict what each prospective customer is actually worth to us over their full lifetime, and feed that value signal back to the platforms so their bidding algorithms spend every dollar optimizing on long-term profitability. Building those value models, and the automated systems that deliver their predictions to our ad platform partners, is the core of this job
• You’ll sit within our Marketing team at the center of our digital acquisition strategy, while working as part of Enova’s broader data science community. You will collaborate especially closely with our Pricing & Profitability team, whose lifetime value and profitability models form the analytical foundation your bidding models will build on
• Beyond VBB, you’ll be the analytical partner to Marketing across all digital channels, helping the team understand what’s working, design experiments that prove it, and allocate budget where it earns the best return
• Build, validate, and maintain machine learning models that estimate customer lifetime value at early funnel stages (click, lead, application) where value signals are needed for real-time bidding
• Design and implement the automated systems that pass value estimates to ad platforms — offline conversion uploads, conversion APIs, and server-side integrations with partners like Google Ads and Meta — and own their reliability, latency, and data quality
• Partner with the Pricing & Profitability team to ensure your value estimates stay consistent with Enova’s core lifetime value and return-on-equity models as they evolve
• Design and analyze experiments (holdouts, geo tests, incrementality studies) that measure whether value-based bidding actually improves acquisition efficiency and portfolio quality
• Act as a data science partner to the broader Marketing team on channel optimization: budget allocation, audience and segmentation strategy, campaign measurement, and funnel analytics across paid search, paid social, and other digital channels
• Communicate insights and recommendations to Marketing and Data Science leadership, providing a data-driven perspective on where and how we grow

Benefits

• Our hybrid roles require in-office work Tuesday through Thursday, with remote flexibility on Mondays and Fridays. This schedule fosters collaboration, team connection, and strategic planning, enhancing communication and effectiveness to drive results
• Health, dental, and vision insurance including mental health benefits
• PTO & paid holidays off
• Sabbatical program (for eligible roles)
• Summer hours (for eligible roles)
• Paid parental leave
• DEI groups (B.L.A.C.K. @ Enova, HOLA @ Enova, Women @ Enova, Pride @ Enova, South Asians @ Enova, APEX @ Enova, and Parents @ Enova)
• Charitable matching and a paid volunteer day…Plus so much more!
• Employee recognition and rewards program
• 401(k) matching plus a roth option

You don’t need a marketing background to succeed here. If you’re a strong data scientist who wants to see your models directly move spend, volume, and profitability every day, we can teach you the domainProficient programming skills with Python and the ability to write customized programs for meaningful data analysisExperience working with relational databases, such as SQLStrong problem-solving skills and the ability to work independently in a fast-paced, dynamic environmentExcellent knowledge of applied statistical methods and machine learning modelsDegree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related field4+ years of experience in Data Science, Analytics, or a related fieldExposure to digital marketing or martech/adtech: paid search or paid social platforms, conversion tracking, attribution, or marketing measurementExperience in finance or lending, lifetime value estimation, or unit economics modelingExperience deploying models into production systems or building automated data pipelines.