Senior Data Analyst

Job Description: Senior Data Analyst(freelancer)

Date of commencement: ASAP

Location: India (remote)

Neoma overview

Neoma is an AI company integrating systems and physical touchpoints to power automated operational workflows and deliver world-class, human-centric experiences.They specialize in seamless identity verification and world-class experiences for VIP guests.

Job brief

We are looking for a Data Analyst to work on our clients projects.

This project is about building an operations control tower for a growing bakery chain, using data and sensors to track products from ingredients through production to instore sales across multiple outlets. It aims to reduce waste, improve product availability on shelves, and optimize store performance through better visibility, analytics, and decision support.

Project description

The initiative starts with a focused pilot on a small number of products and stores, then scales once the value is proven. It combines computer vision, RFID, BLE scales, and POS data to create an endtoend view of production, logistics, instore display, and customer traffic.

In the production and logistics stages, the system measures how raw ingredients translate into finished goods, tracks parcels leaving the central facility and arriving in each store, and quantifies losses and waste at each step. In the store, it monitors tray occupancy and endofday leftovers, counts people outside and entering, and measures queue length and waiting time to understand conversion and service levels.

The project delivers a suite of dashboards: a production view showing ingredient input, theoretical output, and losses as a funnel; a store cockpit with live tray status and waste; a footfall and queue view with conversion and waittime metrics; and an executive view with chainwide KPIs and ROI on waste reduction and sales uplift. The longterm goal is a standard, scalable analytics and monitoring platform that operations, finance, and management teams can use to continuously optimize processes across the network.

Core purpose

The data analyst's core mission is to design, validate, and maintain the data models, KPIs, and analyses needed to measure yield, waste, tray availability, conversion, and queue performance across pilot stores and then at scale.

Data pipeline and modelling

Map all data sources (ingredients, CV counts, RFID events, BLE weights, POS sales) into a coherent data model from input ingredients to final items sold by SKU, batch, store, and time.

Define and maintain data transformations needed to compute theoretical output, stage-by-stage counts, and reconciliations between CCTV/RFID/BLE signals and POS data.

Work with engineers to specify data quality rules (e.g. missing RFID scans, anomalous tray weights, inconsistent counts) and implement validation reports and alerts.

KPI definition and tracking

Formalize KPI definitions such as yield per SKU and batch, waste rate by stage, tray availability, endofday waste, drivetostore conversion, waiting-time metrics, and abandonment rate, ensuring they are consistently calculated across stores and time periods.

Build and maintain KPI tracking for the production, store cockpit, footfall/queue, and executive dashboards, including time-sliced views by store, SKU, time of day, and pilot vs. control groups.

Support ROI quantification by linking operational improvements (waste reduction, improved availability, shorter queues) to revenue uplift and cost savings.

Analysis and insight generation

Analyze ingredient-to-sale funnels to pinpoint where losses occur (prep, oven, packing, transport, display) and estimate waste volumes and percentages by stage and store.

Use tray occupancy and people-counting data to identify lost availability windows, under or overproduction patterns, and opportunities to adjust baking and replenishment schedules.

Study footfall vs. entrants vs. queue metrics to understand conversion, peak congestion, and service bottlenecks, and to estimate potential sales lost due to long waits or stockouts.

Dashboard design and support

Translate stakeholder needs (ops, store managers, executives, finance) into dashboard requirements for the production, store cockpit, footfall/queue, and executive views.

Define the layout, metrics, drilldowns, and filters that make each dashboard actionable, and collaborate with UX and engineers on implementation.

Continuously refine dashboards based on user feedback and pilot learnings to ensure they remain relevant as the project scales.

Stakeholder collaboration and governance

Partner with the ops champion, store trainers, and finance team to validate assumptions, interpret results, and prioritize optimization opportunities.

Support change management by preparing simple analytical narratives (before/after metrics, store rankings, pilot vs. baseline) that help store teams understand and trust the system.

Contribute to data governance by documenting metric definitions, data lineage, and known limitations, and by highlighting privacy/compliance considerations where people-counting or CCTV data are used.

Please send your resume to [Confidential Information] if you are interested in this position.

For more information about us, visit our website: www.neoma.ai.