Sr Data Scientist - Intelligent Media Targeting
Blend360 · Hyderabad · onsite · Posted 45d ago
Undisclosed8–12 yrsfulltimeSourced from
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About this role
Job Description
As a Senior Data Scientist - Intelligent Media Targeting, you will play a key role in designing and deploying advanced customer analytics and marketing science solutions that enable intelligent media planning and customer targeting. You will work on customer segmentation, Marketing Mix Modeling (MMM), causal measurement, marketing effectiveness, and AI-powered analytics to help global clients optimize media investments and drive measurable business outcomes.
This role requires strong expertise in machine learning, statistical modeling, customer analytics, and marketing measurement, along with the ability to translate complex analytical findings into actionable business recommendations. You will collaborate closely with business stakeholders, data engineers, and cross-functional teams to deliver scalable, production-ready data science solutions.
Key Responsibilities
- Design and develop advanced customer segmentation models using clustering techniques such as K-Means, GMM, DBSCAN, or similar algorithms.
- Build and deploy Marketing Mix Models (MMM) to measure marketing effectiveness and optimize media investments.
- Engineer customer and transaction-level features including RFM, spend trajectory, recency decay, and behavioral metrics.
- Develop statistical and machine learning models for customer targeting, campaign optimization, and marketing effectiveness measurement.
- Perform causal inference, experimentation, and A/B testing to measure incremental impact of marketing initiatives.
- Apply model explainability techniques such as SHAP to generate meaningful business insights and customer narratives.
- Develop reusable Python-based analytics frameworks and scalable machine learning workflows.
- Partner with business stakeholders to understand marketing objectives and translate them into analytical solutions.
- Present analytical findings and strategic recommendations to business and leadership teams.
- Collaborate with data engineering teams to build scalable data pipelines and production-ready analytics solutions.
- Contribute to AI-enabled analytics initiatives by leveraging Generative AI, LLMs, or AI-assisted workflows where applicable.
- Mentor junior team members and contribute to analytics best practices and reusable frameworks. Qualifications
Required Skills
Data Science Machine Learning
- Strong hands-on experience in Python development for production-grade analytics and machine learning.
- Extensive experience with pandas, NumPy, scikit-learn, and related data science libraries.
- Strong SQL skills including joins, aggregations, window functions, and large-scale data analysis.
- Solid understanding of statistics including regression, hypothesis testing, probability distributions, model validation, and experimentation.
- Experience developing production-ready machine learning solutions.
Customer Segmentation Marketing Analytics
- Strong experience building customer segmentation models using clustering algorithms such as K-Means, Gaussian Mixture Models (GMM), DBSCAN, or equivalent techniques.
- Experience performing cluster validation using techniques such as Silhouette Score or stability analysis.
- Experience engineering customer features including RFM, spend trajectory, recency decay, and behavioral analytics.
- Experience developing customer personas and translating analytical outputs into business strategies.
- Experience with campaign targeting, customer analytics, recommendation systems, or propensity modeling.
Marketing Science
- Hands-on experience with Marketing Mix Modeling (MMM), Marketing Effectiveness Measurement, or Multi-Touch Attribution (MTA).
- Experience building media optimization, response curve, and budget allocation models.
- Understanding of media carryover, saturation effects, adstock, elasticity, and promotional effectiveness.
- Experience with causal inference, Geo Experiments, incrementality testing, or A/B experimentation.
AI Explainability
- Experience using SHAP, LIME, or other Explainable AI techniques.
- Exposure to Generative AI, LLMs, Prompt Engineering, or AI-assisted analytics workflows.
- Understanding of model governance, monitoring, and deployment best practices.
Qualifications
- Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Economics, or a related quantitative discipline.
- Senior Data Scientist: 5-8 years of relevant experience in Data Science, Customer Analytics, Marketing Analytics, or Marketing Science.
- Lead/Manager: 8-12 years of relevant experience with demonstrated team leadership and stakeholder management.
- Proven experience delivering production-grade machine learning and analytics solutions.
- Strong analytical thinking, problem-solving, and communication skills.
- Experience working directly with business stakeholders and cross-functional teams.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Skills
ai explainabilityCampaignclusteringbusiness insightscampaign optimizationCustomer Segmentationcustomer analyticsAnalyticsData EngineeringCommunication SkillsAlgorithmsdata analysisData PipelineData ScienceArtificial Intelligence
About Blend360
Blend360
IT Services & Consulting
115
employees
2015
11 years old
Hyderabad / Secunderabad, Telangana
India
₹24.3L PA avg
Avg at Blend360
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