Data Scientist III

Wilson•Mountain View, CA

6d•Remote

About The Position

Our client, a global technology leader, is seeking a Data Scientist for FY27. This individual will partner with marketing stakeholders to define and measure key business metrics and identify and execute on opportunities to improve performance and positively impact business results. The role is a 12-month contract with a strong potential for extension, requiring 40 hours per week (Monday-Friday) and paying $85 per hour. The position is fully remote, requiring work during Pacific time hours.

Requirements

7-10 years of overall experience preferred
Experience in CRM, lifecycle marketing, or marketing analytics is highly preferred and considered important for success
SQL Proficiency: Writing complex queries to extract, join, and transform data from relational databases.
Data Visualization Tools: Expertise in tools like Qlik Sense for building dashboards and reports.
ETL Knowledge: Familiarity with data pipelines and tools (e.g., Superglue, Alteryx, Informatica, dbt, Airflow).
Statistical Analysis: Understanding of statistical methods, A/B testing, regression analysis, and forecasting.
Lifecycle Marketing Knowledge
CRM Strategy & Lifecycle Marketing: Understanding of customer lifecycle stages, retention strategies, customer journeys, and lifecycle campaign optimization.
CRM Performance Metrics: Knowledge of KPIs such as open rate, click-through rate (CTR), click-to-open rate (CTOR), conversion rate, unsubscribe rate, churn, retention, customer lifetime value (LTV), and engagement metrics.
Segmentation & Personalization: Experience with audience segmentation, customer targeting, personalization strategies, and dynamic content to improve campaign performance.
Campaign Measurement & Optimization: Ability to measure, analyze, and optimize CRM campaigns across email, push notifications, SMS, in-app messaging, and other owned channels.
Customer Journey Analytics: Understanding of customer behavior, funnel analysis, cohort analysis, and journey performance to identify opportunities for improving engagement and retention.
Experimentation & Testing: Knowledge of A/B and multivariate testing methodologies to evaluate messaging, timing, audience segmentation, and campaign effectiveness.
CRM Platforms & Data Integration: Familiarity with CRM platforms (e.g., Braze) and the integration of customer data for reporting and analytics.
Business Impact Analysis: Ability to connect CRM initiatives to business outcomes, including revenue, customer retention, engagement, and loyalty metrics.
Data & AI Competencies
Dashboard Creation: Designing user-friendly, automated dashboards for marketing stakeholders.
Data Storytelling: Translating raw data into clear insights and actionable recommendations.
Data Governance: Ensuring data accuracy, consistency, and compliance with established standards.
KPI Frameworks: Defining and maintaining standardized marketing metrics and reporting structures.
AI Agent Workflow Automation: Understanding of AI agents and automated workflows to streamline reporting, insights generation, and business process execution.
Prompt Engineering: Ability to design effective prompts and instructions for AI-assisted data analysis and reporting.
Process Automation: Experience identifying repetitive reporting tasks and automating them using AI-enabled tools or workflow platforms.
Human-in-the-Loop Validation: Ensuring AI-generated outputs are reviewed, accurate, and aligned with business requirements.
Soft Skills
Communication: Explaining complex data findings to non-technical stakeholders.
Collaboration: Partnering with marketing, finance, and data engineering teams.
Problem-Solving: Identifying root causes of performance issues and recommending optimizations.
Attention to Detail: Ensuring the accuracy and reliability of reports.
Curiosity & Business Acumen: Proactively identifying trends and opportunities within the data.
Bachelor’s degree in Business Analytics, Marketing Analytics, Statistics, Computer Science, Economics, or a related quantitative field.
Equivalent work experience in data analysis or marketing reporting may be considered in place of formal education.

Nice To Haves

Scripting Languages: Python or R for advanced data analysis and automation
Master’s degree in Data Analytics, Business Intelligence, or Marketing Analytics is a plus.

Responsibilities

Performs business analysis using various techniques, e.g., statistical analysis, explanatory and predictive modelling, and data mining.
Determines best practices and develops actionable insights and recommendations for current business operations.
Works directly with internal or external clients to identify analytical requirements.
Produces ad hoc data analyses and reports.
Assists in implementing or developing systems to capture business operation information.
May occasionally guide less experienced business data analysts.