Role Description We are seeking an experienced Data Scientist to transform complex distributor transaction data into actionable sales opportunities. This role focuses on developing predictive models, recommendation engines, and commercial analytics solutions that directly influence sales strategies and revenue growth. You will work with large-scale B2B datasets containing thousands of customers, products, SKUs, and branch locations, turning raw ERP data into meaningful insights, dashboards, and opportunity recommendations for commercial teams and business leaders. This is a highly impactful role where your work will directly support sales teams, executive decision-making, and business growth initiatives. Key Responsibilities - Advanced Analytics & Machine Learning - Build and optimize cross-sell recommendation engines using market basket analysis, affinity modeling, and peer-based recommendation techniques. - Develop predictive models for: - Product reorder forecasting - Customer churn and retention analysis - Customer lifetime value (CLV) - Lapsed product detection - White-space opportunity identification - Share-of-wallet estimation - Create customer segmentation frameworks to improve sales targeting and account prioritization. - Commercial & Sales Analytics - Conduct sales performance analysis, pricing optimization studies, and customer coverage assessments. - Generate actionable account-level insights that help sales teams identify growth opportunities. - Develop data-driven recommendations that support executive reporting and strategic decision-making. - Data Engineering & Data Quality - Work extensively with ERP-sourced transactional data, including: - Customer master data - Product hierarchies - Invoice-level transactions - Branch and location structures - Clean, standardize, and transform large, complex datasets from multiple sources. - Address challenges such as duplicate records, inconsistent hierarchies, and incomplete data. - Business Intelligence & Visualization - Translate analytical outputs into user-friendly dashboards and reports. - Deliver insights through visualization tools such as Power BI, Tableau, or custom reporting solutions. - Ensure outputs are easily consumable by business users and sales teams. - Stakeholder Communication - Present findings, recommendations, and model outcomes to business leaders and non-technical stakeholders. - Explain complex analytical concepts in clear, practical business language. - Support executive-level discussions with data-backed insights and recommendations. Success Metrics - First 90 Days: Deliver an end-to-end customer opportunity model from raw data ingestion through actionable sales output. - Within 6 Months: Build standardized and reusable recommendation methodologies applicable across multiple business scenarios. - Within 12 Months: Develop scalable analytics frameworks and reusable data science solutions that can be deployed across multiple client environments. Qualifications - 5–9 years of experience in Data Science, Analytics, or Machine Learning roles. - Strong experience working with B2B commercial datasets involving customers, products, transactions, and sales data. - Proven experience developing: - Recommendation systems - Market basket analysis models - Propensity and predictive analytics models - Advanced proficiency in Python and SQL. - Experience handling large-scale transactional datasets with millions of records and extensive product catalogs. - Hands-on experience working with ERP-generated data and complex commercial data structures. - Strong analytical thinking and problem-solving capabilities. - Ability to communicate technical concepts effectively to business stakeholders. Preferred Qualifications - Experience with ERP platforms such as Prophet 21, Eclipse, Kinetic, or similar enterprise systems. - Background in distribution, wholesale, manufacturing, industrial products, retail analytics, or B2B commerce. - Experience building analytics solutions around SKU-level purchasing behavior. - Knowledge of recommendation engines and customer analytics in B2B environments. - Hands-on experience with Power BI, Tableau, or similar visualization tools. - Familiarity with data quality challenges including duplicate records, inconsistent hierarchies, and fragmented transactional data. Key Skills - Must-Have Skills: - Data Science - Machine Learning - Python - SQL - ERP Data Analytics - Recommendation Systems - Predictive Modeling - B2B Analytics - Good-to-Have Skills: - Market Basket Analysis - Customer Segmentation - Power BI - Tableau - Distribution & Wholesale Analytics - Commercial Intelligence - Sales Analytics What Makes This Opportunity Unique - Work on high-impact analytics initiatives that directly influence sales outcomes and business growth. - Gain exposure to executive-level decision-making and strategic commercial initiatives. - Build scalable analytics products and frameworks with real-world business applications. - Operate in a highly autonomous, data-driven environment with significant ownership and visibility.

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