[India]Engagement Lead - MMx | MTA
ProcDNA
- Location
- Pune/Pimpri-Chinchwad Area
- Job type
- Full-time
Required skills
- Tableau
- Python
- Power BI
- business objectives
- cross-functional
- Databricks
- econometrics
- end-to-end
- regression
- Snowflake
- SQL
About the role
ProcDNA
Website:
procdna.com
Job details:
About ProcDNA
ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge technology to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 470+ professionals across 9 offices, all growing and learning together since our launch during the pandemic. Here, you won't be confined to a cubicle - you'll be solving complex business challenges, collaborating with brilliant minds, and shaping the future of healthcare analytics. At ProcDNA, innovation isn't just encouraged - it's ingrained in our DNA. Ready to join our growth journey?
Role Overview
We are seeking a Marketing Mix Analytics Lead to drive measurement and optimization of marketing investments for global clients. This role focuses on Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) to quantify channel impact, evaluate ROI, and support data-driven budget allocation decisions. The ideal candidate will have strong expertise in MMM and MTA, with the ability to translate analytical outputs into clear, actionable business recommendations.
While experience in the pharmaceutical/life sciences domain is preferred, candidates with relevant MMM experience from other industries are also encouraged to apply.
Key Responsibilities- Lead end-to-end Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) engagements, from problem structuring to final recommendations.
- Analyze multi-channel marketing data to quantify channel effectiveness, contribution, attribution, and ROI.
- Apply statistical techniques such as regression modeling, adstock, saturation curves, and attribution methodologies.
- Translate analytical outputs into actionable business insights and budget optimization strategies.
- Develop client-facing presentations with clear storytelling and structured recommendations.
- Collaborate with cross-functional teams to ensure high-quality delivery and alignment with business objectives.
- Support the development of analytical tools and accelerator solutions for MMM, MTA, and reporting workflows.
Required Qualifications- 5-7 years of experience in Marketing Mix Modeling (MMM) and marketing analytics.
- Hands-on experience in Multi-Touch Attribution (MTA) is mandatory.
- Experience in commercial analytics, preferably within consulting or analytics organizations.
- Pharma/Life Sciences experience is preferred but not mandatory. Candidates from other industries with strong MMM and MTA expertise will also be considered.
- Strong foundation in regression, econometrics, statistical modeling, and marketing measurement techniques.
- Proficiency in Python or R, along with SQL.
- Experience working in client-facing roles.
Good to Have- Experience with advanced MMM techniques such as Bayesian modeling, time-series modeling, and other advanced marketing measurement methodologies.
- Exposure to GenAI-based Marketing Mix Analytics functionalities and AI-assisted analytics workflows.
- Experience in developing analytical tools, accelerators, or internal products for marketing analytics use cases.
- Familiarity with measurement frameworks such as Test & Control, incrementality analysis, or causal inference.
- Experience with modern data platforms such as Snowflake or Databricks, and visualization tools such as Power BI or Tableau.
Key Competencies- Strong analytical and problem-solving skills.
- Ability to translate complex analytical findings into clear business recommendations.
- Excellent communication and stakeholder management skills.
- High ownership, collaboration, and delivery focus.
Preferred Background- Experience in consulting, analytics, or marketing effectiveness roles with hands-on exposure to MMM and MTA.
- Experience in the pharmaceutical/life sciences industry is preferred but not mandatory. Candidates from other domains with strong marketing measurement expertise will also be considered.
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