Marketing Data Science Consultant

I. About the Role

Accelerize 360 is an AI-enabled consulting and technology firm serving clients across Banking, Insurance, Retail, and Sports. We're looking for a Data Scientist to join our marketing practice, applying statistical and machine learning methods to help clients get more value from their customer data — from campaign performance and attribution to segmentation, propensity modeling, and personalization at scale.

You'll work directly with client data across a variety of technology stacks, translating messy, real-world marketing data into models and insights that drive measurable outcomes. This is a hands-on, delivery-facing role — you'll be part of our broader Marketing AI practice, Consulting, and Technology teams to embed data science into client engagements from discovery through deployment.

II. About you

You're someone who's genuinely energized by marketing data - the kind of person who sees a messy customer dataset as a puzzle worth solving, not a chore. You approach new tools and techniques with a beginner's mindset, comfortable picking up an unfamiliar platform or method when it's the right fit for the problem rather than sticking to what's familiar.

You're analytical and methodical by nature, working through ambiguous problems step by step rather than jumping to conclusions — but you also operate with a sense of urgency, with the organizational habits and follow-through to keep client-facing work moving on schedule. You build relationships easily with clients and colleagues, and you look for ways to proactively improve the team's work, not just your own individual output. Mentoring and knowledge-sharing come naturally to you, whether that's explaining a model's logic to a non-technical stakeholder or helping a peer debug an approach.

You're comfortable with ambiguity and enjoy being part of a practice that's still being built, where your technical judgment directly shapes how A360 approaches marketing data problems.

III. Primary Responsibilities

• Design, build, and validate statistical and machine learning models supporting marketing use cases — including segmentation, propensity/churn modeling, attribution, lifetime value, and personalization
• Work with client marketing data across various platforms, including but not limited to Salesforce Data Cloud and Marketing Cloud, to extract, clean, and structure data for analysis
• Partner with practice leads and consultants to translate business questions into analytical approaches and measurable outcomes
• Present findings and recommendations to both technical and non-technical stakeholders, including client executives
• Collaborate with engineering and delivery teams to operationalize models into production workflows and marketing platforms
• Contribute to the development of reusable analytical frameworks, accelerators, and methodologies for the marketing practice
• Stay current on emerging techniques in marketing data science, including applied AI/ML approaches relevant to personalization and customer engagement
• Support proposal development and scoping for engagements requiring data science expertise

IV. Required Qualifications

• 3+ years of experience in data science, applied statistics, or analytics, with meaningful exposure to marketing use cases
• Proficiency in Python or R, SQL, and standard data science tooling (e.g., pandas, scikit-learn, or equivalent)
• Experience building models for segmentation, propensity, attribution, or personalization
• Familiarity with marketing automation or CDP platforms (e.g., Salesforce Marketing Cloud, Salesforce Data Cloud, Adobe Experience Platform, or similar) — hands-on experience a plus
• Ability to communicate technical findings clearly to non-technical audiences
• Experience working in a consulting, agency, or client-services environment a plus
• Bachelor's or advanced degree in a quantitative field (Statistics, Computer Science, Data Science, Economics, or related) or equivalent practical experience

Skills Required
Pandas, Salesforce Marketing Cloud, Sql, Python