Senior Data Scientist – Intelligent Media Targeting

Posted 3 Days Ago
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Hyderabad, Telangana, IND
In-Office
Senior level
Database • Analytics
The Role
Design and deploy customer analytics and marketing science solutions (segmentation, MMM, causal inference, A/B testing). Engineer features, build production-ready ML workflows, apply explainability (SHAP/LIME), partner with stakeholders, and mentor junior staff to optimize media investments and measurement.
Summary Generated by Built In
Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

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

  • Bachelor's or Master's 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.

Additional Information

Nice to Have

  • Experience working with payment transaction or card network data.
  • Understanding of Spend Index, Wallet Share, BIN/ICA logic, or merchant hierarchy.
  • Experience with Spark, Databricks, or distributed data processing.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Exposure to Graph Analytics, Recommendation Systems, or Knowledge Graphs.
  • Relevant certifications in Data Science, Machine Learning, Cloud, or AI technologies.

Thrive & Grow with Us

  • Competitive Salary: Your skills and contributions are highly valued here, and we make sure your salary reflects that, rewarding you fairly for the knowledge and experience you bring to the table.
  • Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly, providing the right tools, mentorship, and experiences to fast-track your career.
  • Idea Tanks: Innovation lives here. Our "Idea Tanks" are your playground to pitch, experiment, and collaborate on ideas that can shape the future.
  • Growth Chats: Dive into our casual "Growth Chats" where you can learn from the best—whether it's over lunch or during a laid-back session with peers, it's the perfect space to grow your skills.
  • Snack Zone: Stay fuelled and inspired! In our Snack Zone, you'll find a variety of snacks to keep your energy high and ideas flowing.
  • Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives, shoutouts, and the chance to see your ideas come to life as part of our reward program.
  • Fuel Your Growth Journey with Certifications: We're all about your growth! Enhance your expertise with company-sponsored certifications in AI, Data Science, Cloud, and Analytics technologies.

Skills Required

  • Production-grade Python development for analytics and machine learning
  • Experience with pandas, NumPy, and scikit-learn
  • Strong SQL skills (joins, aggregations, window functions, large-scale analysis)
  • Solid understanding of statistics (regression, hypothesis testing, distributions, model validation, experimentation)
  • Experience developing production-ready machine learning solutions
  • Experience building customer segmentation models (K-Means, GMM, DBSCAN) and cluster validation
  • Experience engineering customer and transaction-level features (RFM, spend trajectory, recency decay, behavioral metrics)
  • Experience developing customer personas and translating analytics into business strategy
  • Experience with campaign targeting, recommendation systems, or propensity modeling
  • Hands-on experience with Marketing Mix Modeling (MMM), Marketing Effectiveness Measurement, or Multi-Touch Attribution
  • Understanding of media carryover, saturation, adstock, elasticity, and promotional effectiveness
  • Experience with causal inference, Geo experiments, incrementality testing, or A/B experimentation
  • Experience using explainable AI techniques such as SHAP or LIME
  • Exposure to Generative AI, LLMs, or prompt engineering and AI-assisted analytics workflows
  • Understanding of model governance, monitoring, and deployment best practices
  • Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Economics, or related quantitative discipline
  • 5-8 years relevant experience in Data Science, Customer Analytics, Marketing Analytics, or Marketing Science (Senior level)
  • Experience with payment transaction or card network data
  • Understanding of Spend Index, Wallet Share, BIN/ICA logic, or merchant hierarchy
  • Experience with Spark or Databricks and distributed data processing
  • Experience with cloud platforms such as AWS, Azure, or GCP
  • Exposure to Graph Analytics, Knowledge Graphs, or advanced recommendation systems
  • Relevant certifications in Data Science, Machine Learning, Cloud, or AI technologies

Blend360 Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.

  • Fair & Transparent Compensation Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
  • Flexible Benefits Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
  • Retirement Support A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.

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The Company
HQ: Columbia, MD
390 Employees
Year Founded: 2016

What We Do

Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.

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