Senior Manager, Data Science Consulting

Overview

You work between sales, product, and client data science teams to prove Nova Credit's value through data. You're analyzing client portfolios, building custom underwriting models using Nova's alternative credit data, and showing financial institutions how cash flow or international credit data improves their approval rates and reduces risk. This is technical sales engineering with heavy data science work.

Role Snapshot

Company Context

Stage: Series D ($156M raised, recent $35M round in Oct 2025)

Size: 109 employees

Growth: Expanding cash flow underwriting product (Cash Atlas), recent BMO partnership, focused on serving banks, fintechs, credit unions, auto lenders

Market Position: Category creator in cross-border credit and alternative data infrastructure - competing against traditional credit bureaus (Experian, TransUnion) and newer cash flow players (Plaid, Finicity, Zest AI)

GTM Reality

Pipeline Sources:

• 60% Enterprise outbound - AEs targeting specific banks/lenders with whale hunting approach
• 30% Partnership referrals - existing clients expanding to new products
• 10% Inbound - conference leads, industry press from partnerships

SDR/AE Structure: AEs self-source at this stage, you support deals once they reach technical evaluation

SE Support: You ARE the technical support - likely working across multiple concurrent deals

Competitive Landscape

Main Competitors:

• Traditional bureaus (Experian, TransUnion, Equifax) for core credit
• Plaid/Finicity for cash flow/bank data
• Zest AI for alternative scoring models
• Point-solution providers (Credit Kudos, Borrowell internationally)

How They Differentiate: Single platform accessing multiple alternative data sources (international credit, cash flow, income, documents) vs stitching together multiple vendors

Common Objections:

• "We already have Plaid for bank data"
• "How accurate is international credit data?"
• "Compliance and regulatory concerns with new data sources"
• "Integration complexity with existing decisioning systems"

Win Themes: Unlock underserved segments (immigrants, thin-file consumers), reduce fraud, improve approval rates while maintaining risk profile

What You'll Actually Do

Time Breakdown

Custom Analyses (35%) | Deal Support (30%) | Model Building (20%) | Internal (15%)

Key Activities

• Portfolio Analysis: Banks send you anonymized application data, you run it through Nova's models to show lift in approval rates or reduction in default risk. Building Excel models and slide decks showing "If you had used Nova Credit, you would have approved X% more applicants with Y% default rate."
• Custom Proof-of-Concept: Building underwriting models using client data + Nova data sources. Writing Python/R code to demonstrate how Cash Atlas or Credit Passport improves their decisions. These take 2-4 weeks and are make-or-break for deals.
• Technical Discovery: Joining sales calls with bank CROs, chief data scientists, and risk teams. Asking detailed questions about their current underwriting models, data sources, approval rates, and loss rates to identify where Nova adds value.
• Implementation Planning: Once deal is won, scoping out the technical integration - API specs, data mapping, testing phases. Working with their engineering and compliance teams on 3-6 month rollout plans.

The Honest Reality

What's Hard

• Analysis Requests That Go Nowhere: You'll spend 10-20 hours on custom analyses for deals that ultimately don't close because procurement killed it, budget disappeared, or they went dark. Banks move slowly.
• Data Quality Issues: Client data is messy. You spend time cleaning it before you can even start the analysis. International credit data has gaps. You're constantly explaining data limitations and building confidence intervals.
• Regulatory/Compliance Blockers: Financial services is heavily regulated. Your beautiful model might get killed by the legal team who's worried about FCRA compliance or using alternative data. You don't control these conversations.
• Complex Internal Coordination: You need data/API access from Nova's engineering team, you need product input on roadmap, you need the AE to get you client data. Lots of dependencies to get your work done.

What Success Looks Like

• Your POCs have an 80%+ close rate when they make it to completion
• Deals you support have 30%+ higher win rates than deals without technical validation
• Clients reference your analysis in their internal business case to buy Nova Credit

Who You're Selling To

Primary Buyers:

• Chief Risk Officers and VP of Credit Risk at banks/lenders
• Heads of Data Science and Analytics teams
• VP of Lending or Consumer Lending decision-makers

What They Care About:

• Approval rate lift while maintaining current default rates (or vice versa)
• Regulatory compliance and audit trail for using alternative data
• Integration effort and time to production
• Data accuracy and coverage (especially for international credit or thin-file consumers)
• ROI calculation - will this generate more revenue than it costs?

Requirements

• 5+ years in data science with at least 2 years in financial services (credit risk modeling, underwriting analytics)
• Strong Python/R, SQL, and statistical modeling - you're building actual models, not just presenting
• Experience with credit risk concepts (FICO scores, default rates, loss curves, approval rates)
• Can translate technical data science into business impact for non-technical buyers
• Pre-sales or client-facing analytics experience (you need to present and defend your work on calls)
• Comfortable with ambiguity - you're figuring out what analysis will convince each specific client