Data Scientist — Marketplace Products

NorthLadder is building the AI-native backbone of the global secondary market for electronics — a marketplace where every device's true value is known before it's even returned, and where the industry looks to us as the source of truth for what used electronics are worth. We're building the pricing, matching, and valuation intelligence that makes a marketplace of physically unique, ever-depreciating assets behave like a liquid, efficient market.

What you'll do

• Build and own predictive models across the marketplace's core intelligence layer — spanning device valuation, demand and supply forecasting, channel and matching optimization, and cost modeling

• Apply LLMs and generative AI techniques where they're the right tool — from extracting structure out of messy unstructured data to powering natural-language interfaces over marketplace data — alongside traditional predictive modeling, not instead of it

• Shape the product, not just the model — think through what a given model actually needs to do for the people using it, and bring that thinking into design decisions alongside modeling ones

• Partner directly with commercial and pricing teams to translate model outputs into decisions that hold up in the real world, not just in a validation set

• Define what “good” looks like for each model — accuracy targets, confidence thresholds, and how a model's outputs should degrade gracefully when it's uncertain, rather than silently failing

• Work iteratively — this is a build-and-expand role:



you'll start with our highest-priority model and grow into a broader scope as it matures and as new priorities emerge

What we're looking for

• 5–6 years of experience in applied data science or machine learning, with real models shipped into production, not just research or prototypes

• Robust applied modeling skills — forecasting, regression-based modeling, classification/ranking problems — and comfort owning a model end-to-end, not just the training step

• Working experience with LLMs and generative AI — prompt-based approaches, fine-tuning, or applied use of foundation models in a real product context

• Genuine product thinking — this can come from formal product experience, or simply be evident in how you've approached past modeling work: asking who the model serves and what decision it drives, not just what accuracy it hits. We're open on background here and will judge this on your actual experience, not a specific title you've held

- Strong Python and SQL; comfortable working with real-world, messy transactional data
- Able to work closely with commercial stakeholders — explaining model behavior, trade-offs, and limitations in terms a non-technical audience can act on
- Comfortable with ambiguity — this role's scope will expand over time, and priorities will shift as the highest-value problem changes

Nice to have

- Experience with valuation, pricing, or depreciation modeling specifically
- Experience in marketplace, e-commerce, or resale/secondary-market businesses
- Familiarity with matching/recommendation systems or multi-sided marketplace dynamics

Interested candidates can fill up the form Data Scientist – Fill out form or share their profile at [email protected]

📌 Data Scientist (India)
🏢 NorthLadder
📍 India