JOBWAVE VERIFIED

Senior Data Scientist — Marketing Analytics

Company: Not specified in the supplied description
Role Title: Data Scientist, Marketing Analytics
Job Identification: 71121
Location: 15 Plot No. 13, Chennai, Tamil Nadu 600119, India
Work Mode: On-site
Employment Type: Full-time
Experience Required: 5+ years
Job Category: Enterprise Technology
Posting Date: 21 October 2026, 12:09 AM
Application Deadline: 23 October 2026, 12:00 AM
Minimum Education: Bachelor's degree or equivalent in a quantitative field
Preferred Education: Master's degree
Salary: Not specified

Core Technology & Analytics Stack

• Programming: Python, R, SQL
• Statistical Methods: Experimental design, causal inference, predictive modeling, time-series analysis
• Marketing Analytics: CRM analytics, lifecycle marketing, campaign measurement, customer analytics
• Media Platforms: Google Ads, Search Ads 360, Meta, Display & Video 360 (DV360)
• Measurement: Incrementality, holdout experiments, lift measurement, A/B/n testing
• CRM Metrics: Engagement, churn, customer lifetime value (LTV), lifecycle performance
• Media Metrics: Cost, conversion, efficiency, return on ad spend (ROAS)
• Forecasting: Campaign performance forecasting and target setting
• AI & Automation: LLMs, AI copilots, AutoML, automated reporting, code assistance, feature engineering
• Collaboration: Marketing teams, media buying, marketing mix modeling (MMM), CRM teams, data engineering, agencies, and data partners

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JOBWAVE INSIGHTS

1. Who Is This Role For?

This is a senior-level data science role focused on marketing measurement, experimentation, and campaign effectiveness.

The ideal candidate combines statistical expertise with hands-on marketing data experience. They should be able to determine whether marketing campaigns actually cause incremental business results, rather than simply reporting clicks, conversions, and revenue.

The role supports North American CRM and performance media programs while being based on-site in Chennai. It involves collaboration with US-based data science peers, data engineering, marketing teams, agencies, and media partners.

Main Areas of Responsibility

1. Campaign Measurement: Design measurement frameworks for CRM and paid media campaigns.
2. Causal Inference: Determine the incremental impact of marketing activities using experiments and statistical models.
3. Experimentation: Design A/B/n tests, holdouts, and lift studies.
4. Performance Forecasting: Forecast campaign outcomes and support target setting.
5. Marketing Data Integration: Combine media platform data with internal customer and sales data.
6. Business Communication: Turn complex analysis into practical recommendations for marketing stakeholders.
7. AI-Enabled Analytics: Use AI tools to automate reporting, support coding, and improve analytical workflows.
8. Mentoring: Guide junior analysts and data scientists on analytics best practices.

2. Key Skills Required

Highest Priority

• At least 5 years of applied data science experience.
• Direct experience in CRM marketing and/or performance media analytics.
• Strong SQL and Python or R skills.
• Statistical experimentation and causal inference.
• A/B/n testing, holdout design, and incrementality measurement.
• Experience working with advertising platform data.
• Ability to combine campaign data with customer and sales data.
• Understanding of CRM metrics, media performance, and business outcomes.
• Strong communication and stakeholder influence.

Strong Advantages

• Experience with Google Ads, Search Ads 360, Meta, and DV360.
• Customer lifetime value, churn, engagement, and lifecycle analytics.
• Marketing mix modeling (MMM) and collaboration with media buying teams.
• Predictive modeling and time-series forecasting.
• AI/ML-assisted analytics, LLMs, copilots, and AutoML.
• Automated reporting and feature engineering workflows.
• A master's degree in a quantitative discipline.

Important: Generic data science experience alone may not be sufficient. Direct marketing analytics and advertising platform data experience are explicitly emphasized in the requirements.

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JOB DETAILS

3. Role Overview

The Senior Data Scientist will establish measurement and analytics approaches for CRM marketing and performance media campaigns targeting North America.

The main objective is to understand which campaigns drive measurable incremental outcomes, improve marketing efficiency, support forecasting, and help marketing teams make better decisions.

This position combines statistical modeling, experimentation, marketing data engineering collaboration, AI-enabled automation, and senior stakeholder communication.

4. Main Responsibilities

A. Marketing Measurement & Experimentation

• Design measurement frameworks for email, push notifications, lifecycle campaigns, paid search, social, and display advertising.
• Develop incrementality measurement approaches, holdout strategies, and lift studies.
• Design and analyze A/B/n experiments.
• Build causal inference models to quantify campaign impact.
• Distinguish campaign-attributed outcomes from outcomes genuinely caused by marketing activity.

B. Marketing Analytics & Forecasting

• Analyze CRM engagement, churn, LTV, and lifecycle performance.
• Evaluate media cost, conversion, efficiency, and ROAS.
• Support marketing performance forecasts and campaign target setting.
• Combine advertising data with internal customer and sales data.
• Build a holistic performance view across owned and paid channels.

C. Data Quality & Cross-Functional Collaboration

• Partner with agencies and data partners on tracking and data quality.
• Maintain consistent measurement taxonomies across marketing channels.
• Collaborate with data engineering, CRM, media buying, MMM, and marketing teams.
• Work with US-based data science peers to align analytical approaches and business outcomes.

D. AI & Analytics Automation

• Evaluate AI tools that improve analytical workflows.
• Automate reporting and repetitive data tasks.
• Use AI assistance for coding and feature engineering.
• Explore approaches that increase the speed and quality of insights while maintaining analytical rigor.

E. Communication & Mentoring

• Present complex analytical findings in clear, actionable language.
• Influence daily marketing decisions through data.
• Mentor junior analysts and data scientists.
• Establish and promote sound analytical practices.

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REQUIREMENTS

5. Core Requirements

Requirement	What the Employer Is Looking For
Experience	5+ years of applied data science
Domain	CRM marketing and/or performance media analytics
Programming	SQL and Python or R
Statistical expertise	Experimental design, causal inference, predictive modeling, time-series
Campaign data	Direct experience with media platform data
Data integration	Combining media, customer, and sales data
CRM knowledge	Engagement, churn, LTV, lifecycle metrics
Media knowledge	Cost, conversions, efficiency, ROAS
Communication	Clear recommendations and stakeholder influence
Education	Bachelor's degree or equivalent in a quantitative field
Preferred qualification	Master's degree

6. Technical Concepts to Prepare

Marketing Measurement

• Incrementality versus attribution.
• Holdout groups and control groups.
• Absolute and relative lift.
• A/B and A/B/n testing.
• Statistical significance, confidence intervals, and statistical power.
• Selection bias, confounding, and causal inference.
• Cross-channel measurement and consistent campaign taxonomies.

Marketing Analytics

• Customer acquisition and conversion funnels.
• Email and push engagement metrics.
• Customer retention, churn, and lifetime value.
• Cost per acquisition (CPA), conversion rate, and ROAS.
• Paid media performance across search, social, and display.
• Campaign forecasting and target setting.

Data Science & Engineering

• SQL joins, aggregations, window functions, and data validation.
• Python/R for statistical analysis and modeling.
• Feature engineering and predictive modeling.
• Time-series forecasting and seasonality.
• Data quality checks and reconciliation across platforms.
• AI-assisted analysis, coding, and reporting automation.

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QUALIFICATIONS

7. Candidate Fit Checklist

• 5+ years of applied data science experience.
• Direct experience with CRM or performance media analytics.
• Hands-on access to and analysis of Google Ads, Meta, SA360, DV360, or comparable platform data.
• Experience combining advertising data with customer and sales data.
• Practical experience designing experiments and measuring incrementality.
• Strong SQL and Python/R proficiency.
• Understanding of CRM metrics and media performance signals.
• Experience presenting insights to non-technical stakeholders.
• Familiarity with AI/ML tools and analytics automation.
• Bachelor's degree or equivalent in a quantitative field.

8. Potential Gaps to Check

Marketing domain gap: A candidate with strong general-purpose machine learning experience but little marketing analytics exposure may struggle with the role's campaign measurement requirements.

Causal inference gap: Reporting campaign performance is different from proving that a campaign caused incremental conversions or revenue. Prepare examples of experimental or causal measurement.

Media platform gap: Be ready to explain how you accessed, cleaned, joined, and analyzed data from advertising platforms.

Business impact gap: Demonstrate how your findings changed campaign budgets, targeting, channel allocation, or customer strategy.

Forecasting gap: Prepare an example of campaign or time-series forecasting, including validation and treatment of seasonality.

AI tooling gap: Be prepared to explain where AI automation helped your workflow and how you checked the quality and correctness of the output.

Education gap: The minimum stated qualification is a bachelor's degree or equivalent in a quantitative field. A master's degree is preferred, not stated as mandatory.

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INTERVIEW PREPARATION

Phase 1 — SQL, Python/R & Data Analysis

Prepare for:

• SQL joins, CTEs, window functions, aggregation, and cohort analysis.
• Combining advertising, customer, sales, and CRM datasets.
• Handling missing values, duplicate records, and inconsistent tracking.
• Python/R for statistical analysis and visualization.
• Feature engineering and model evaluation.
• Data validation and reconciliation across different data sources.

Practice task: Combine campaign spend, impressions, clicks, conversions, and customer sales data to produce a daily channel-performance report with conversion rate, CPA, and ROAS.

Phase 2 — Statistics & Causal Inference

Focus on:

• Experimental design and A/B/n testing.
• Hypothesis testing, confidence intervals, and statistical power.
• Randomized holdout groups and lift measurement.
• Incrementality versus attribution.
• Confounding, selection bias, and causal inference.
• Predictive modeling and time-series forecasting.
• Communicating uncertainty and limitations.

Practice task: A paid campaign shows increased conversions after launch. Explain how you would determine whether the campaign caused the increase rather than simply receiving credit for conversions that would have occurred anyway.

Phase 3 — CRM & Performance Media Analytics

Prepare to discuss:

• Email, push, and lifecycle campaign measurement.
• Customer engagement, churn, retention, and LTV.
• Google Ads, Search Ads 360, Meta, and DV360 data.
• Spend, CPA, conversion rates, ROAS, and campaign efficiency.
• Attribution limitations and cross-channel overlap.
• Marketing mix modeling and collaboration with media buying teams.
• Forecasting campaign performance and setting realistic targets.

Practice task: Design a measurement framework for a campaign that uses email, paid search, and social advertising. Explain how you would measure channel performance, incremental impact, and overall business results.

Phase 4 — AI Automation & Stakeholder Leadership

Prepare examples involving:

• Automating recurring campaign reports.
• Using LLMs or copilots to assist with code and analysis.
• Applying AI/ML tooling to feature engineering or analytical workflows.
• Validating AI-generated code and preventing incorrect conclusions.
• Explaining statistical findings to non-technical marketing teams.
• Resolving disagreements about campaign effectiveness.
• Mentoring junior data scientists and analysts.

Practice task: Propose an AI-assisted workflow that reduces the time required to produce weekly marketing insights while preserving data quality, reproducibility, and analytical accuracy.

Final Interview Focus

Prioritize causal inference, incrementality measurement, hands-on media platform data, SQL/Python or R, and business-oriented marketing analytics.

Prepare three detailed project examples:

1. A campaign experiment or lift measurement project.
2. An analysis that combined advertising data with customer or sales outcomes.
3. A forecasting, automation, or predictive modeling project that improved a business decision.

For each, explain the business question, data sources, methodology, assumptions, validation, results, and the decision influenced by the analysis.

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APPLICATION SUMMARY

Position: Senior Data Scientist — Marketing Analytics
Job ID: 71121
Location: Chennai, Tamil Nadu, India
Work Mode: On-site
Employment: Full-time
Experience: 5+ years
Education: Bachelor's degree or equivalent in a quantitative field
Preferred Degree: Master's
Application Deadline: 23 October 2026, 12:00 AM, as listed in the supplied job information

Overall recommendation: A strong fit for experienced data scientists who specialize in CRM or paid-media analytics and can connect rigorous statistical measurement to marketing decisions. Candidates should prioritize practical campaign experimentation and incrementality examples over generic machine learning projects.

Information not provided: Employer name, salary range, recruiter contact, and application URL.