Manager Data Scientist - MMM

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. We help organisations solve complex business challenges by combining deep domain understanding with modern data and AI capabilities. Our teams work across strategy, analytics, engineering, and product delivery to create scalable, high-value solutions that improve decision-making, efficiency, and growth.

We are seeking a highly skilled and experienced Manager Data Scientist with expertise in Media Mix Modeling (MMM) to lead advanced analytics initiatives and drive data-driven marketing effectiveness strategies. The ideal candidate will combine strong technical expertise with leadership capabilities to manage projects, mentor teams, and partner with business stakeholders to deliver actionable insights that optimize marketing investments across channels.

Key Responsibilities:

• Lead the development and implementation of Media Mix Models to optimize marketing spend across various channels (e.g., TV, digital, radio, print, social, etc.).
• Manage end-to-end analytics projects including problem definition, data acquisition, modeling, validation, and business recommendations.
• Partner with marketing, finance, and business leadership teams to align analytical solutions with strategic business objectives.
• Build and oversee predictive and forecasting models to evaluate future marketing scenarios and guide budget allocation decisions.
• Mentor and guide junior data scientists and analysts by providing technical leadership, code reviews, and best practices in analytics and modeling.
• Present analytical findings, recommendations, and business impact clearly to senior stakeholders and executive leadership.
• Drive deep-dive analyses on campaign performance, customer behavior, and media effectiveness to identify optimization opportunities.
• Ensure accuracy, consistency, and integrity of data, models, and analytical outputs across projects.
• Collaborate with cross-functional teams including Data Engineering, BI, Marketing, Product, and external partners to ensure seamless execution of analytics initiatives.
• Establish scalable MMM frameworks, methodologies, and governance standards across the organization.
• Stay updated on emerging trends in marketing analytics, causal inference, econometrics, machine learning, and MMM methodologies.
• Create and maintain comprehensive documentation for models, processes, methodologies, and business assumptions.

• Bachelor’s or Master’s degree in Data Science, Statistics, Economics, Mathematics, Computer Science, or a related quantitative field.
• Proven experience (8+ years) in Media Mix Modeling (MMM), marketing analytics, or advanced statistical modeling.
• Strong expertise in statistical techniques such as regression analysis, time-series modeling, econometrics, and forecasting.
• Hands-on experience with Python, R, SQL, and data visualization tools such as Tableau or Power BI.
• Experience leading analytics projects and mentoring or managing teams in a fast-paced environment.
• Strong understanding of marketing and media data sources (e.g., Nielsen, IRI, CRM, digital media platforms, social media data, etc.).
• Ability to translate complex analytical outputs into strategic business recommendations for senior stakeholders.
• Excellent stakeholder management, communication, and presentation skills.
• Strong problem-solving mindset with the ability to manage multiple priorities and deliver high-impact solutions.
• Familiarity with machine learning techniques and experimentation frameworks is an added advantage.

Preferred Qualifications:

• Experience working with large-scale datasets and cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of causal inference frameworks, marketing attribution, customer segmentation, and lifetime value (LTV) analysis.
• Experience designing and evaluating A/B tests and controlled experiments.
• Prior experience in consulting, marketing analytics, or agency environments.
• Exposure to team management, project planning, and client-facing engagements.
• Experience working in global or cross-functional delivery models.