Lead Data Scientist (Growth & Performance Marketing)

SDLC Technologies; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Sdlc Technologies is seeking a Lead Data Scientist (Growth & Performance Marketing) to elevate our marketing analytics and attribution practice. You will provide senior leadership in building sophisticated marketing measurement systems and coach the data science team to optimize customer acquisition and lifetime value.

You will set roadmaps, establish standards for causal inference, and collaborate with marketing, finance, and product leaders to align investments with business outcomes.

Qualifications

• 10+ years of professional experience in data science/analytics with proficiency in Python, SQL, or R.
• 3+ years leading marketing analytics or growth data science teams.
• Deep expertise in marketing attribution methodologies (MMM, MTA, incrementality testing).
• Excitement to leverage the latest LLM tools to improve workflows and efficiency.
• Strong experience with Bayesian methods for hierarchical modeling and ridge regression.
• Proven track record of building and deploying marketing measurement systems at scale.
• Experience with customer lifetime value modeling and cohort-based forecasting.
• Deep experience in building robust data models for marketing analytics.
• Experience mentoring data scientists on marketing analytics best practices.
• Excellent communication skills with ability to translate complex attribution insights to marketing teams.
• Strong business acumen with ability to connect marketing metrics to financial outcomes.

Responsibilities

• Set the roadmap and model best practices for marketing and growth sciences.
• Lead the development of unified marketing measurement approaches combining Marketing Mix Modeling (MMM), Multi-touch Attribution (MTA), and incrementality testing.
• Partner with marketing leadership and C‑Suite to optimize marketing spend allocation and Return on Ad Spend (RoAS).
• Establish and evangelize methodological standards for marketing measurement and causal inference.
• Build frameworks for Customer Lifetime Value (CLV) modeling and customer acquisition cost (CAC) optimization.
• Coach and mentor data scientists on advanced attribution techniques and marketing analytics.
• Develop and maintain marketing attribution models using tools like Robyn and custom solutions.
• Lead geo‑experimentation and matched market testing initiatives.
• Build self‑service dashboards and tools for marketing performance monitoring.
• Collaborate with finance on budgeting and forecasting models.
• Champion data‑driven decision making in growth strategy and channel optimization.
• Represent the data science team in marketing strategy and budget planning discussions.

Skills

Python

SQL

R

Leadership

Marketing attribution

MMM

MTA

Incrementality testing

LLM tools

Bayesian modeling

CLV modeling

Cohort forecasting

Marketing analytics

Robyn

Tools

Robyn

Job description

Lead Data Scientist (Growth & Performance Marketing)

We are looking for a Lead Data Scientist (Growth & Performance Marketing) to join our team and help elevate our marketing analytics and attribution practice. This role will provide senior leadership in building sophisticated marketing measurement systems, establishing methodological rigor for growth analytics, and coaching our data science team to deliver high-impact solutions that optimize customer acquisition and lifetime value. You have strong opinions about what world‑class marketing attribution looks like and are passionate about driving efficient growth. You will work across marketing, finance, and product teams and have a keen sense of how to connect marketing investments to business outcomes and customer value.

Responsibilities

• Set the roadmap and model best practices for marketing and growth sciences.
• Lead the development of unified marketing measurement approaches combining Marketing Mix Modeling (MMM), Multi-touch Attribution (MTA), and incrementality testing
• Partner with marketing leadership and C‑Suite to optimize marketing spend allocation and Return on Ad Spend (RoAS)
• Establish and evangelize methodological standards for marketing measurement and causal inference
• Build frameworks for Customer Lifetime Value (CLV) modeling and customer acquisition cost (CAC) optimization
• Coach and mentor data scientists on advanced attribution techniques and marketing analytics
• Develop and maintain marketing attribution models using tools like Robyn and custom solutions
• Lead geo‑experimentation and matched market testing initiatives
• Build self‑service dashboards and tools for marketing performance monitoring
• Collaborate with finance on budgeting and forecasting models
• Champion data‑driven decision making in growth strategy and channel optimization
• Represent the data science team in marketing strategy and budget planning discussions

Key Qualifications:

• 10+ years of professional experience in data science/analytics with demonstrated proficiency in Python, SQL, or R and statistical modeling
• 3+ years of experience leading marketing analytics or growth data science teams
• Deep expertise in marketing attribution methodologies (MMM, MTA, incrementality testing)
• Excitement to leverage the latest LLM tools to improve workflows and efficiency.
• Strong experience with Bayesian methods for hierarchical modeling and ridge regression
• Proven track record of building and deploying marketing measurement systems at scale
• Experience with customer lifetime value modeling and cohort‑based forecasting
• Deep experience in building robust data models for marketing analytics
• Experience mentoring data scientists on marketing analytics best practices
• Excellent communication skills with ability to translate complex attribution insights to marketing teams
• Strong business acumen with ability to connect marketing metrics to financial outcomes

Nice‑to‑have:

• Experience in media/streaming industry with understanding of subscription business models
• Knowledge of time‑series forecasting and predictive modeling for demand planning
• Experience with propensity modeling and customer segmentation
• Familiarity with privacy‑preserving measurement techniques (differential privacy, data clean rooms)
• Background in econometric modeling and quasi‑experimental methods
• Experience with survey design and analysis for brand lift and market research
• Knowledge of modern martech stack integration and CDP platforms