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Job Description:

Data Scientist (Short-term & Long-term MMM)

Job Group Level: 5

Role Expectations:

• Overall 5+ years of experience in Data Science, Analytics, ML
  • 3+ years of hands-on experience in Marketing Mix Modelling required (both ST & LT)
• Incumbent is responsible for developing analytical models for projects, collaborating with different business stakeholders & other partners and working across a range of technologies and tools.
  • The primary focus will be on developing and maintaining short-term and long-term MMM models, with opportunities to work on a variety of other marketing analytics use cases.
• Good knowledge of Marketing domain, ATL/BTL marketing and clear understanding of concepts like adstock/carryover, saturation etc.
  • Clear understanding of Marketing Conversion funnel (awareness, consideration etc.)
• End-to-end MMM solution development - Gathering requirements, Data Processing, EDA, Modelling (both Short-Term & Long-Term Impact), Optimization etc.
• Deep understanding of various algorithms applicable in MMM (additive, multiplicative models, Bayesian Regression etc.)
• Good knowledge of open-source MMM libraries like PyMC, Robyn, Meridian etc.
• Well versed with optimization techniques and prior experience of solving media budget optimization problems in Python/R
  • Well versed with multi-objective optimization algorithms and implementation in Python/R
• Strong coding skills in Python is a must. Ability to develop production level codes with best coding practices.
• Ability to collaboratively work across multiple sets of stakeholders –business SMEs, Digital Product Owners, IT, Data teams, Analytics professionals, Data Engineers etc. to deliver on project deliverables and tasks.
• Use storytelling skills to articulate business insights and recommendations (based on model output) to senior business stakeholders through presentations

Desirable Industry/Functional Skills:

• Industry Experience in Oil & Gas, Downstream business, Mobility, Retail, CPG or FMCG business would be desirable.
• Clear understanding of Marketing Conversion funnel (awareness, consideration etc.)
• Understanding of upper and lower funnel channels for delivering the right message based on campaign objectives.
• Experience across key areas of Marketing Analytics, including customer and campaign analytics, attribution and incrementality, product & pricing insights, and CRM and loyalty analytics.

Required Technical Skills:

• Deep expertise in machine learning techniques (supervised and unsupervised) Statistics / Mathematics / Operations Research / Computer Science including:
  • Statistics / Mathematics: Data Quality Analysis, Data identification, Hypothesis testing, Univariate / Multivariate Analysis, Clustering, Classification/PCA, Linear Modeling, Multiplicative Modelling (log-log, log-linear), Time Series Analysis, distribution / probability theory, Calculus, Linear Algebra, Optimization.
  • Advanced Machine learning techniques: Bayesian Linear Regression, Causal ML, Decision Trees, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Feature Reduction / engineering
  • Computer Science (Good to have): Functional Programming, Object Oriented Programming, System Design, SOLID Principles, Design Patterns, Version Control (Git)
• Strong experience in specialized analytics tools and technologies:
  • Python – Essential
  • SQL - Essential
  • R & Power BI/other visualization experience – Good to have
• Proven experience in building MMM models to capture both ST & LT impact of Marketing on Sales and Brand Equity is a must.
• Deep understanding of various algorithms applicable in MMM (additive, multiplicative models, Bayesian Regression etc.)
• Understanding of Brand Funnel: Brand Media -> Brand Equity (Awareness, Preference) -> Sales. Experienced in capturing impact of Brand Media on each stage of the brand funnel using long-term Modelling.
• Expert knowledge of open-source MMM libraries like PyMC, Robyn, Meridian etc.
• Well versed with optimization techniques and prior experience of solving media budget optimization problems in Python/R
• Well versed with multi-objective optimization algorithms and implementation in Python/R

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