About the Role :

Revenue Optics turns distributor transaction data into selling opportunities. Our clients are US distributors with 50,000+ SKUs, 100 to 300+ branch locations, and tens of thousands of business customers. You will take their raw customer transaction data and build the models that tell a salesperson exactly which customer to call and what to sell them : cross-sell recommendation algorithms, lapsed product detection, reorder prediction, customer segmentation, share of wallet estimation, and coverage gap analysis.

Your models do not sit in a notebook. They become dashboards and call lists that inside sales reps act on the same week, and analysis that goes into steering committee decks reviewed by CEOs and private equity sponsors. You will see your work move revenue at real companies.

What You Will Own :

- Cross-sell recommendation engines : market basket and peer-based recommendation models across catalogues of 50,000+ SKUs, designed for B2B purchasing behaviour

- Customer opportunity models : lapsed product detection, reorder prediction, churn risk, customer lifetime value, and white space scoring across large account bases

- Commercial diagnostics : coverage analysis, sales rep performance quartile analysis, pricing dispersion, and share of wallet modelling that feed client executive readouts

- Data engineering for messy reality : distributor data comes from aging ERPs with inconsistent product hierarchies and dirty customer records - you make it usable

- Sales-facing delivery : translate model output into dashboards and account-level opportunity lists a salesperson can act on without a statistics degree

What We Require (Hard Requirements) :

- 5 to 9 years in data science with B2B commercial data : transactions, customers, products, sales.

- Production experience with recommendation systems, market basket analysis, or propensity modelling on large transactional datasets.

- Strong Python and SQL, comfort with large messy datasets.

- ERP-sourced commercial data fluency : has personally worked with raw transactional data extracted from an ERP or core commerce system.

- Ability to explain a model to a non-technical sales leader in plain English and defend an analysis in front of executives.

- Sustained US shift commitment : 7am4pm Central (approx. 5 : 30pm2 : 30am IST)