Job Description

Senior ManagerEXL/SM/1362307


ServicesGurgaon
Posted On
19 May 2025
End Date
03 Jul 2025
Required Experience
8 - 12 Years



Basic Section
Number Of Positions


1


Band


C2


Band Name


Senior Manager


Cost Code


D014043


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


1500000.0000 - 2500000.0000


Complexity Level


Not Applicable


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


Analytics


Sub Group


Analytics - UK & Europe


Organization


Services


LOB


Services


SBU


Analytics


Country


India


City


Gurgaon


Center


EXL - Gurgaon Center 38




Skills
Skill


POWER BI


ML


SQL


PYTHON


Minimum Qualification


B.COM


Certification


No data available




We are looking for an experienced

Data Scientist

with a strong background in

Retail & E-commerce Analytics

, particularly in integrating

offline store data

into analytics solutions. The ideal candidate will leverage

General Analytics

,

AI/ML

, and other advanced data science techniques to bridge the gap between online and offline retail data, enabling smarter business decisions and enhancing customer experiences across both channels.

Responsibilities:

Offline Store Data Analytics

:




Analyze and integrate

offline store data

(sales, foot traffic, inventory, etc.) with

online

data to provide a holistic view of customer behavior and sales trends.




Use

AI/ML

models to optimize offline store performance by forecasting demand, inventory needs, and staffing levels based on historical data.




Analyze the impact of

offline marketing campaigns

and promotions on in-store foot traffic and sales, using predictive analytics and machine learning.




Develop models to predict store performance, taking into account various factors like location, weather, local events, and other external variables.

Retail & E-commerce Analytics

:




Use

machine learning algorithms

and

statistical analysis

to understand and predict consumer behavior, both in-store and online.




Build and maintain models for

customer segmentation

,

personalized marketing

, and

sales forecasting

for both e-commerce and brick-and-mortar stores.




Identify key metrics for store performance and customer satisfaction, helping management teams optimize strategies for in-store and online experiences.

AI/ML Implementation for Retail Optimization

:




Apply

AI/ML techniques

(e.g.,

classification

,

regression

,

clustering

,

time series forecasting

) to retail data, enabling actionable insights for improving product assortment, pricing, and promotions both online and offline.




Develop demand forecasting models for

offline stores

, ensuring optimal stock levels based on predicted customer needs and sales trends.




Use

machine learning

to enhance

inventory management

in offline stores by predicting inventory shortages and surplus.

Data Integration & Visualization

:




Work with large datasets from both

offline

and

online sources

to clean, integrate, and analyze the data, ensuring data accuracy and consistency.




Develop dashboards and visualizations using tools like

Tableau

,

Power BI

, or

Google Data Studio

to communicate key findings and business insights to stakeholders.




Create reports that combine insights from offline and online channels, providing a unified view of retail operations.

Campaign Performance Analysis

:




Use data science techniques to analyze the effectiveness of

offline marketing campaigns

and promotions on both in-store and online traffic, conversion rates, and sales.




Evaluate customer engagement with offline campaigns, offering insights into how online and offline channels influence each other.

Collaboration with Cross-Functional Teams

:

Work closely with retail managers, marketing teams, and IT teams to implement data-driven solutions that improve customer experience and business performance.




Collaborate with product, marketing, and supply chain teams to optimize the

omnichannel strategy

, including aligning online and offline inventories and promotions.

Key Technical Skills:

Machine Learning & AI

:




Proficiency in

Python

or

R

for building and deploying

AI/ML models

such as

random forests

,

XGBoost

,

SVM

, and

neural networks

.




Strong experience in applying

predictive modeling

,

regression analysis

, and

time series forecasting

to retail data, including demand forecasting and sales prediction.




Familiarity with

deep learning

techniques (e.g.,

RNNs

,

LSTMs

) for more complex data patterns, if relevant.

Retail & E-commerce Analytics

:




Experience in analyzing

point-of-sale (POS) data

,

foot traffic data

, and

customer journey

data for offline retail stores.




Understanding of

e-commerce KPIs

and how they integrate with offline store performance metrics.




Strong knowledge of

inventory optimization

,

supply chain management

, and how they relate to both online and offline retail operations.

Big Data & Data Integration

:




Proficiency in handling and analyzing large datasets from multiple sources (online and offline) using

SQL

,

NoSQL

, and cloud platforms like

AWS

,

GCP

, or

Azure

.




Ability to work with

ETL processes

to integrate data from multiple systems, ensuring high-quality data for analysis.

Data Visualization & Reporting

:




Experience with

Tableau

,

Power BI

,

Google Data Studio

, or other visualization tools to present insights and actionable business recommendations.




Strong communication skills to present complex findings to both technical and non-technical stakeholders.

Statistical Analysis

:




Proficiency in

statistical methods

for hypothesis testing, segmentation analysis, and measuring the effectiveness of retail strategies and campaigns.




Familiarity with advanced statistical techniques, including

Bayesian methods

,

Monte Carlo simulations

, and

multivariate testing

.

Desired Qualifications:

Bachelor's or Master's degree in

Computer Science

,

Data Science

,

Statistics

,

Engineering

, or a related field.




5+ years of experience in

data science

or

analytics

in the

retail

or

e-commerce

industry, with a focus on

offline store performance

.




Proven experience in applying

AI/ML models

to solve real-world business problems, including demand forecasting, personalization, and campaign optimization.




Familiarity with

offline store data

(sales, foot traffic, inventory) and how it integrates with

e-commerce platforms

.




Strong understanding of the retail industry, particularly in optimizing performance across

omnichannel environments

(online and offline).

Workflow
Workflow Type


Back Office