Lead Data Analyst

Rrdonnelley; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Rrdonnelley seeks a Lead Data Analyst to guide measurement strategies and translate data into innovative analytics frameworks for cross-channel success. You will partner with clients to design, implement, and evolve analytics programs across verticals.

Responsibilities include leading multi-channel data integrations, developing insights frameworks, and mentoring others in advanced analytics techniques. Strong communication and client-facing skills are essential.

Qualifications

• Minimum 4-year quantitative degree; advanced analytics preferred.
• Proven track record in marketing analytics and data-driven decision making.
• Strong programming, modeling and data extraction capabilities.

Responsibilities

• Co-create marketing analytics strategies with client leaders.
• Translate behavioral data into scalable analytics frameworks.
• Lead cross-functional analytics initiatives and insights projects.
• Represent analytics in client consultations and solution design.
• Provide thought leadership and mentor junior analysts.
• Oversee marketing science initiatives including test frameworks and attribution.

Skills

Python

R

SQL

Machine Learning

Segmentation

Forecasting

Generative AI

Leadership

Education

Bachelor's degree in a quantitative field

Graduate degree

Tools

Snowflake

AWS

Databricks

Microsoft Fabric

PySpark

Snowpark

Pandas

Azure DevOps

Git

BigQuery

Tableau

Power BI

SiSense

Job description

The Lead Data Analyst serves as a strategic advisor, guiding measurement strategies and translating data into innovative frameworks for cross-channel success. This roles partners directly with clients to design, implement, and evolve marketing & data analytics programs. It leads engagements that require integration of multi-channel data, development of insights frameworks, and translates analytics into business outcomes across verticals.

Responsibilities

• Partner with client leaders to co-create marketing analytics strategies that support personalization, attribution, and media mix optimization.
• Translate complex customer behavior data into scalable frameworks that inform future marketing innovation across channels.
• Lead cross-functional analytics initiatives and enterprise-wide insights projects.
• Represent analytics expertise in insight development, client consultations, strategic planning, and solution design.
• Provide thought leadership across domains and mentor others in advanced analytics techniques.
• Design and oversee strategic marketing science initiatives, including test frameworks, predictive modeling, and audience performance measurement.

Education

• 4 Year / Bachelors Degree In a quantitative field (Minimum)
• Graduate Degree (Preferred)

Experience

• 9 years experience in Advanced Marketing or Product Analytics roles
• Industry experience: Business vertical experience in. Retail, Financial Services, Automotive , or CPG strongly preferred.

Programming & Data Extraction

• Expertise in Python and/or R. Strong SQL proficiency is mandatory; candidates must be able to read data models, write efficient queries, and independently extract data for analytic projects with limited assistance.

Statistical & Modeling Acumen

• Must possess a strong foundation in probability theory, as well as linear, logistic, and multinomial regression. Candidates must be able to develop custom models using modern machine learning ensembling techniques and software, as well as building custom solutions directly in R and Python.

Spezialized Modeling

• Must have experience building customer and/or store segmentation models. A forecasting background with the ability to build custom ARIMAX models, along with Marketing Mix and Multi-Touch Attribution experience, is strongly preferred. Causal analysis experience is highly desirable.

Market Research Knowledge

• Foundational understanding of attitudinal research and segmentation approaches. Ideal candidates will have developed behavioral models predicting attitudinal segments.

Generative AI

• Comfortable using generative AI to develop code, conduct research, summarize methodology, and to develop client-facing presentations.

Interpersonal, Leadership & Consulting Skills (Required)

• Communication & Presentation: Strong communication and storytelling skills are an absolute must for this role. Candidates must be highly comfortable presenting to Executive-level clients.
• Consultative Problem-Solving: Able to nimbly propose solutions to client problems. Although this is not a sales role, consultative selling skills are required for success.
• Client Engagement: Strong active listening and documentation skills are required. The individual in this role must be able to identify solutions for clients that extend beyond their current request.
• Leadership & Autonomy: While this is an Individual Contributor role, the expectation is that the individual will be able to lead projects and oversee the work of more junior analysts. Must be highly resourceful, capable of developing relationships with Marketing Technologies/IT and Account Management, and able to independently develop solutions with minimal supervision.
• Work Style: Thrives in a dynamic, fast-paced environment. Energized by new challenges with an optimistic outlook, resilient to changes, and nimble in shifting direction quickly as needed.

Advanced Technical Stack & Tools (Flexible/Preferred)

• Cloud & Database Platforms: Experience with modern cloud database solutions such as Snowflake, AWS, Databricks, Microsoft Fabric, etc. Direct experience using Microsoft Fabric to build models for clients would be ideal.
• Modern Data Stack: Strongly prefer experience with model development in a modern technology stack, specifically PySpark, Snowpark, and Pandas.
• Data Architecture: Understanding of graph databases and NoSQL, alongside experience working with semi-structured and unstructured data, is strongly desired.
• Data Engineering & MLOps: Azure DevOps, Git, or similar version control background is essential. Must have basic data engineering skills and be comfortable collaborating with data engineers to develop production processes.
• Web Analytics Integration: Google Analytics and BigQuery experience. Candidates must know how to integrate customer engagement data into analytic and modeling projects.
• Business Intelligence & Visualization: Familiarity with Business Intelligence solutions such as Tableau, Power BI, SiSense, and other platforms. The ideal candidate would h