Mantle Solutions- Data Scientist-L2

Job Title:

Senior Data Scientist

Reports to:

VP Analytics

Units/Location:

Mantle Solutions (P) Ltd

Bangalore

Main Responsibility / Job Summary

(Primary deliverables of role and its scope)

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• Build and maintain dashboards and performance reports across retail categories, pricing, and promotions.
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• Conduct structured analyses on promo effectiveness, pricing impact, and merchandising performance.
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• Monitor key retail KPIs — sales, basket size, customer retention, and category performance — and flag anomalies.
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• Support cohort analysis and customer segmentation to identify growth and retention opportunities.
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• Collaborate with merchandising, marketing, and operations teams to respond to business data needs.
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• Work with data engineering teams to validate data quality and ensure availability from retail data sources.
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• Translate business questions into SQL queries and deliver clear, well-structured analytical outputs.
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• Prepare insight presentations for business stakeholders and contribute to leadership review decks.
Main Purpose of Job (Why the Job exists / What it must achieve)\n
\n We are looking for a mid-level Retail Analytics professional who can independently own analysis and reporting across the retail business. You will work closely with business teams in merchandising, promotions, pricing, and customer analytics to surface actionable insights and support data-driven decisions. This is an individual contributor role with a strong execution focus — building reports, running analyses, and translating data into clear findings without requiring constant direction. You are expected to be comfortable working across functions and taking full ownership of your deliverables end to end.\n
MOP (Measurable parameters of the role) & Dimensions (Span of the role)\n
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Staff Reporting : 0 Direct Reports : 0\n
Level \n
\n Level 2 | 2–3 Years Experience | Individual Contributor\n
Skills & Knowledge Requirements (Abilities/knowledge & Expertise in field)\n
Knowledge (Technical/Functional)\n
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• Strong working knowledge of retail KPI frameworks — sales, basket size, conversion, retention, LTV.
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• Solid understanding of promo analytics, pricing analytics, and merchandising analytics in a retail context.
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• Familiarity with data warehouse concepts and retail data sources (POS, CRM, digital platforms).
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• Understanding of customer analytics — RFM segmentation, cohort analysis, and basket analysis.
  
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Skills: \n
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• 2–3 years of experience in retail analytics, merchandising analytics, or a closely related domain.
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• Strong SQL skills — able to independently write complex queries, joins, and aggregations.
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• Hands-on experience with at least one BI tool (Power BI or Tableau) for building and publishing dashboards.
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• Working knowledge of Python or PySpark at a basic to intermediate level for data manipulation.
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• Good communication skills — able to present data findings clearly to business stakeholders.
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• Comfortable working in fast-paced, ambiguous environments with a self-driven, proactive approach.
  
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Good to Have: \n
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• Exposure to cloud data platforms such as Azure Databricks or similar environments.
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• Familiarity with A/B testing concepts and statistical interpretation of experiment results.
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• Experience with product affinity modelling or personalisation analytics.
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• Knowledge of Jira or any project/work management tool.
  
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  Qualifications
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  • B.Tech / B.Sc in Statistics, Mathematics, Computer Science or related field
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  • MBA (Analytics / Marketing) is an added advantage
    
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  Experience
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  • 2–3 Years in Retail / eCommerce Analytics
    
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