Job Specifications
Job Description
What is the Opportunity?
The Advanced Data Insights & Integration (ADII) Team of Personal Banking is seeking a passionate and innovative Marketing Science Analyst to join our Marketing Optimization & Planning pillar. This is a role that bridges marketing and business expertise with technical analytics capabilities. You will be responsible for identifying business opportunities, evaluating data sources, defining modeling requirements, and partnering with Data Scientists to expand our marketing measurement capabilities. The ideal candidate combines marketing/media domain knowledge with hands-on analytical skills to drive innovation in how we measure and optimize marketing effectiveness.
What will you do?
Partner with Marketing and Media teams to translate business challenges into structured analytical problems, identifying opportunities to expand measurement capabilities (e.g., controlled experimentation design, upper vs. lower funnel effect, portfolio-level product marketing analysis, geographic modeling)
Investigate and evaluate new data sources for marketing mix modeling and other analytics use cases, such as media execution and attribution metrics, third-party competitive and market research, internal CRM, web analytics, and transaction data
Assess data quality, completeness, and granularity of new data sources; determine which metrics should be incorporated into models and why (e.g., impressions for reach-based analysis)
Collaborate with Data Scientists to define model specifications, variable selection, and transformation logic, and recommend new modeling capabilities such as geographic models, impression-based models, and channel interaction effects
Design and analyze marketing experiments (geo tests, holdout tests) to validate model recommendations and measure incrementality
Validate model inputs and outputs from a business perspective; conduct deep-dive analyses to explain model findings and identify drivers of KPI changes
Create visualizations and dashboards using Tableau, or Python/R libraries
Serve as liaison between Marketing/Media teams and Data Science team; translate technical model outputs into actionable marketing recommendations and present insights to cross-functional audiences
Train marketing teams on interpreting MMM results and using optimization tools; create documentation and manage relationships with external media agencies and data vendors
What do you need to succeed?
Must-have
Bachelor's degree in Marketing, Business, Economics, Statistics, Mathematics, or related field; Master's degree (MBA, MS in Marketing Analytics) preferred
3-5 years of experience in marketing analytics, media planning, digital marketing, or marketing strategy roles, preferably in financial services
Strong understanding of media planning and buying across traditional (TV, radio, print, OOH) and digital channels (display, video, social, search, programmatic); knowledge of media metrics (impressions, reach, GRPs, CPM, CPA, ROAS)
Python proficiency: hands-on experience with pandas, NumPy, matplotlib/seaborn for data analysis and visualization
SQL foundation: strong ability to write complex queries including joins, aggregations, and CTEs
Experience working with AWS (S3, Glue) or other cloud platforms; comfortable with cloud-based notebook environments (Jupyter, SageMaker)
Hands-on experience analyzing media agency data and reconciling discrepancies; familiarity with marketing analytics platforms (Google Analytics, Adobe Analytics, Google/Meta Ads Manager)
Understanding of data quality concepts, validation techniques, and ETL/ELT processes; experience with Git/GitHub for version control
Strong business acumen with ability to connect analytics to business outcomes; excellent communication skills to explain complex concepts to non-technical audiences
Stakeholder management experience working with cross-functional teams (Marketing, Data, Finance, Agencies); problem-solving mindset comfortable with ambiguity; self-starter who proactively identifies opportunities
Nice to have
Experience with marketing mix modeling tools (Meta Robyn, Google Meridian) or exposure to MMM projects; understanding of causal inference concepts (incrementality, test-and-learn)
R programming experience with tidyverse, dplyr, ggplot2
Advanced data visualization skills with Tableau, Power BI, Looker; ability to build executive-level dashboards
Familiarity with programmatic advertising and DSP platforms (e.g. DV360, Amazon DSP)
Experience conducting geo-experiments, matched market tests, or A/B testing programs
Understanding of customer lifetime value (CLV) modeling and attribution models (multi-touch, data-driven)
Experience working in agile/scrum methodologies or contributing to open-source analytics projects
What’s in it for you?
We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care abou