Senior Manager, Advanced Marketing Analytics

Zendesk
9 months ago
Applications closed

Related Jobs

View all jobs

Sales Operations Specialist

Data Engineer

Supply Chain Analyst

Technology and Data Analyst, Career Development Centre

Senior Data Manager

Insurance Trading & Operations Manager

Job Description

We are searching for an experienced and results-driven Head of Marketing Data Science to build and lead our marketing data science team. The ideal candidate will be an expert at using data to drive GTM strategies, understand customer behavior, and measure marketing and GTM performance. The successful candidate will be responsible for overseeing the development and implementation of advanced analytics, data mining, and statistical modeling techniques to solve complex marketing and GTM challenges and drive business growth.

Key Responsibilities

  • Leadership and Team Management:

    • Build, lead, and mentor a team of data scientists and analysts across several locations including outside of the US.

  • Data Strategy and Analytics:

    • Develop and manage in-house predictive models (lead scoring, account scoring, recommendation systems, etc.) and segmentation strategies.

    • Develop and manage lead, contact and account targeting and prioritization frameworks.

    • Leverage predictive modeling and machine learning to forecast trends and customer behavior.

    • Develop and implement a comprehensive data strategy that supports marketing and SDR decision-making and GTM optimization.

    • Oversee the design and execution of experiments and A/B tests to drive continuous improvement in marketing effectiveness.

  • Performance Measurement:

    • Design and maintain core dashboards and reports that track KPIs and ROI.

    • Analyze campaign performance and customer engagement metrics to identify opportunities for growth and optimization.

    • Analyze in-product user journeys and provide recommendations to product and marketing teams.  

    • Provide actionable insights and strategic recommendations to marketing and executive leadership teams.

  • Technology and Innovation:

    • Stay abreast of the latest trends and advancements in marketing analytics and data science.

    • Evaluate and recommend new tools, technologies, and methodologies to improve the team's capabilities and efficiency.

    • Collaborate with IT and engineering teams to enhance data infrastructure and analytics platforms.

Qualifications

  • Master's degree or PhD in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, or related field) or equivalent work experience.

  • 7+ years of experience in data science or a related field, with a proven track record in marketing analytics.

  • Strong leadership skills and experience managing a team of data professionals.

  • Deep expertise in data mining, statistical analysis, machine learning, and predictive modeling.

  • Proficient with data science and analytics tools (e.g., Python, R, SQL, Tableau, Power BI).

  • Excellent communication and interpersonal skills, with the ability to translate complex data into actionable business insights.

  • Strategic thinking with a strong problem-solving ability.

  • Familiarity with CRM systems and marketing automation tools.

Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.

Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.