Role Objective: The AGM Market Research, Analytics & Applied AI will turn raw market data, consumer signals, and platform metrics into direct revenue and cost-saving actions. Rather than delivering static reports, this role focuses on building automated workflows, deploying AI tools (like LLMs for qualitative synthesis and machine learning models for churn/LTV), and translating cross-channel insights directly into campaigns, pricing adjustments, and product pipeline decisions

KEY RESPONSIBILITIES

1. Applied AI & Data Automation

- AI-Assisted Synthesis: Use LLM-based workflows and NLP tools to automatically parse, summarize, and extract actionable sentiment from thousands of e-commerce customer reviews, social conversations, and qualitative survey transcripts.
- Predictive Modeling: Build and deploy practical predictive models (e.g., customer churn risk, repeat purchase probability, and customer lifetime value) using first-party CRM data to trigger automated marketing workflows.
- Prompt & Workflow Optimization: Design and test standard AI prompts and automated data pipelines to reduce the time taken to generate weekly/monthly performance summaries from days to hours.

2. Execution-Focused Market Research

- Action-Oriented Primary Studies: Design lean, targeted quantitative and qualitative studies (Usage & Attitude, Concept Testing, Pack/Price Sensitivity) specifically structured to answer immediate business questions
- Syndicated Data Extraction: Pull and reconcile offline retail data (Nielsen/Kantar) with e-commerce signals (Amazon Pi, Flipkart, 1DS) into a single, unified view to spot channel-specific share shifts and stock-out risks.
- Competitive Scraping & Tracking: Implement automated scraping or web-tracking tools to monitor competitor price changes,



discount depth, stock availability, and keyword ad spend in near real time.

3. Analytics, Attribution & Revenue Drivers

- Automated Dashboards: Maintain daily/weekly automated dashboards (Looker Studio) connecting ad spend (CAC) to sales conversion and bottom-line margin - cutting out manual Excel reporting.
- Media & Incrementality Testing: Run plug-and-play incrementality tests (geo-experiments, holdout groups) and Media Mix Models (MMM) to prove which channels actually drive incremental sales versus claiming credit through view-through conversions.
- D2C & First-Party Data Utilization: Segment first-party data from mywellnesskart.com to create real-time audience cohorts for retargeting, cross-selling, and personalized WhatsApp/Email automation.

4. Stakeholder Execution & Governance

- Direct Business Decision Support: Work directly with Brand, Sales, and E-commerce leads to provide clear, binary recommendations
- Vendor & Tool Efficiency: Manage research agencies and MarTech vendors with strict SLAs. Push vendors to adopt modern data collection methods and ensure all raw data rights stay with Sebamed.

5. People & Capability Building

- AI & Data Literacy: Train the broader marketing and sales teams on basic AI tooling and data self-service, eliminating ad-hoc data pull requests.

STAKEHOLDER INTERACTION

Internally Interacts With:

Business Head, Head of Marketing,



Digital Marketing, E-commerce/Quick Commerce Leads, Brand Managers, Finance

Nature of Interaction:

Providing data-backed recommendations on budgets, pricing, and campaign execution; reviewing weekly performance metrics.

Externally Interacts With:

Research agencies, platform data teams (Amazon, Flipkart), MarTech & AI vendors

Nature of Interaction:

Vendor management, API/data connection setup, SLA enforcement, and sourcing specialized AI/analytics tools.

JOB REQUIREMENTS

Qualified Experience and Relevant Skills

- Experience: 58 years in data analytics, consumer insights, or growth marketing, ideally within FMCG, Beauty, Personal Care, or D2C brands.
- AI & Prompting: Hands-on experience using generative AI tools, LLM APIs, or custom GPTs for text analysis, script writing, or data transformation.
- Data & Analytics Stack:
- Strong proficiency in SQL (ability to query raw databases independently).
- Hands-on experience with visualization tools (Power BI, Tableau, or Looker Studio).
- Working knowledge of Python or R for predictive modeling (regression, clustering, classification) is highly preferred.

- E-Commerce & Digital Data: Direct experience handling GA4, Amazon Brand Analytics, platform-native seller tools, and performance ad metrics.
- Practical Research Methodologies: Direct experience structuring fast, decision-ready surveys and reading syndicated trade data (Nielsen/Kantar).

Academic Qualifications & Certifications

- Degree: Bachelors degree in Statistics, Computer Science, Economics, Engineering, or a related quantitative field.
- Preferred: Master's or MBA with an emphasis on Analytics, Data Science, or Quantitative Marketing.

📌 AGM or Senior Manager (Market Research and Analytics) - Consumer Care (Navi Mumbai)
🏢 USV
📍 Navi Mumbai