Data Science - Global Marketing Engines and Experiences (E&E)

Microsoft•Redmond, WA

2d•$119,800 - $261,000

About The Position

The Global Marketing Engines and Experiences (E&E) team within Microsoft builds and operates globally scaled capabilities that deliver connected marketing journeys. The Marketing Analytics and Data Science team enables data-driven decisions through products and insights that measure marketing impact, deepen understanding of customer behavior, and improve marketing efficiency and return on investment. We are looking for a Senior Marketing Data Scientist – Journey & Signal Intelligence who thrives at the intersection of customer behavior, advanced modeling, and marketing decisioning. You will expand how Microsoft understands the customer journey by integrating cross-channel customer behavior and product signals—including web engagement, product usage, trials, skilling, content consumption, and event activity—into Marketing Engagement Intelligence and other marketing models. You will both investigate and build: working hands-on with complex data, determining which behaviors contain meaningful signal, developing journey and targeting capabilities, and establishing whether they improve marketing and business outcomes. You will also provide technical leadership, shape production requirements, and partner with Data Engineering and marketing teams to move successful concepts into scaled use. We are seeking someone who is curious, comfortable with ambiguity, rigorous about evidence, and committed to shipping useful analytical capabilities. You build on the work of others, value cross-team collaboration, and contribute to a diverse and inclusive workplace.

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
Experience discovering, profiling, and analytically integrating behavioral, interaction, product telemetry, web, or event-level data.
Proficiency with SQL and Python or R, with experience in modern cloud analytics environments such as Azure, Fabric, or equivalent platforms.
Ability to structure ambiguous problems, develop reusable analytical solutions, and communicate effectively with business, engineering, and executive stakeholders.

Nice To Haves

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science rr related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
Experience with B2B marketing, customer journeys, audience strategy, digital engagement, account or lead data, or sales-funnel analytics.
Experience with web analytics, product usage, trials, skilling, content consumption, intent, or other cross-channel customer behavior signals.
Experience building features or models used for targeting, recommendations, journey progression, next-best action, or activation.
Knowledge of MLOps practices and privacy, security, and data-protection requirements for customer and behavioral data.

Responsibilities

Identify and prioritize customer behavior and product data that can improve journey understanding, targeting, and marketing decisions. Profile structured and semi-structured sources and evaluate coverage, identity resolution, quality, bias, latency, stability, and fitness for modeling or activation. Define features, signal taxonomies, normalization methods, and analytical integration patterns; translate validated prototypes into clear requirements for Data Engineering.
Develop models and analytical frameworks that map journeys, identify progression and intent, and reveal meaningful engagement patterns across touchpoints. Create and evaluate audience-selection methods, targeting signals, reusable features, and scores for marketing and activation systems. Partner with marketers to translate model outputs into practical journey and activation decisions that are understandable, actionable, and measurable.
Design experiments and measurement frameworks to determine whether new signals and interventions improve engagement, progression, marketing efficiency, and business outcomes. Establish reusable standards for source evaluation, validation, explainability, drift monitoring, scientific documentation, and production readiness. Collaborate across data science, engineering, taxonomy, reporting, activation, and marketing teams; communicate findings, uncertainty, and recommendations to technical and executive audiences.
Embody our Culture and Values.