Data Scientist

Fa Erqb Saasfaprod1 Open

Remote Irving Full-time Mid-level Data Science

At a glance

Mid-level Data Science role at Fa Erqb Saasfaprod1. Remote, United States · full-time · no salary stated · we estimate $116,300–$174,200 across the United States.

Estimated pay

Fa Erqb Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.

$116,300 – $174,200

What the middle half of mid-level data science roles in the United States paid, among postings that did state a salary.

Worked out from 493 postings that stated pay, median $142,065. Last updated Nov 13, 2026.

RoleData Science

SeniorityMid-level

LocationIrving

WorkplaceRemote

EmploymentFull-time

PostedNov 4, 2026

Role brief

remoteroles summary, based on the employer's posting.

What you'll do

Build models for customer lifetime value, acquisition likelihood and churn using statistical methods, machine learning and customer segmentation
Help develop marketing mix and multi-touch attribution models so leaders can assess media efficiency
Test digital campaigns, creative and landing-page personalization with controlled experiments and hypothesis testing
Turn complex model results into clear visuals and narratives that support executive decisions
Work with engineering teams on pipelines combining ad-platform, web analytics and offline sales data

What you bring

At least three years applying data science and predictive modeling while building end-to-end pipelines for marketing or customer acquisition
A bachelor’s degree or equivalent experience in a quantitative field such as analytics, computer science or statistics
Advanced Python and SQL skills, including work with modeling packages and large, high-dimensional consumer datasets
Experience with Vertex AI or Colab notebooks, plus knowledge of digital marketing channels and web analytics

Who this fits

This role suits a mid-level data scientist who can connect predictive analytics with marketing decisions and explain technical findings to executives. The position is remote from a home office, with occasional travel under a quarter of the time.

From the employer

Summary:

The Data Scientist, Marketing Analytics serves as a core analytical authority on ADT’s marketing efforts, translating complex customer, campaign, and media data into high-value, predictive, and actionable insights. This role bridges the gap between advanced data science and strategic marketing operations, working with stakeholders to solve critical business problems like predicting high-value customer acquisitions, optimizing media spend, and supporting our company's growth, customer lifetime value (LTV), and digital personalization objectives.

The ideal candidate combines strong technical capabilities in machine learning, statistical modeling, and data engineering with an understanding of consumer behavior, marketing technology (MarTech), and executive storytelling. They will work alongside the Marketing Analytics Leader, data analysts, data engineers, and channel managers to build scalable, evidence-based solutions that directly influence ADT’s customer acquisition and retention strategies.

Duties and Responsibilities:

Predictive Growth Modeling: Apply advanced statistical methods, machine learning (e.g., classification, survival analysis), and clustering to predict customer lifetime value (LTV), model acquisition propensity, and identify customer churn risk.
Attribution & Mix Modeling: Support the development of Marketing Mix Modeling (MMM) and multi-touch attribution (MTA) frameworks to help business leaders understand and act on marketing efficiency issues.
Campaign Experimentation: Design and execute evidence-based research and scientific hypothesis testing (A/B and multivariate testing) around digital marketing campaigns, creative assets, and landing page personalization.
Executive Data Storytelling: Translate highly complex statistical findings into clear, beautiful, and intuitive visualizations and compelling narratives that drive strategy, action, and decision-making at executive levels.
Data Engineering & Pipelines: Collaborate with data platform and engineering teams to design, implement, and maintain robust data pipelines that integrate ad-platform APIs, web analytics, and offline sales data into a continuous, single-source-of-truth.
Privacy & Compliance: Implement and champion rigorous data privacy standards and ethical data collection practices, navigating how to effectively target and optimize campaigns while respecting consumer privacy regulations (e.g., CCPA/CPRA, cookie changes).
Cross-Functional Collaboration: Partner directly with Brand, Digital Media, Demand Gen, and Product Marketing teams to frame vague marketing challenges into structured, measurable data science initiatives.
Continuous Improvement: Stay current on evolving marketing technology trends, MLOps best practices, and innovative data sources to elevate ADT's analytical maturity from descriptive reporting to prescriptive action.
Customer Segmentation: Experience creating and defining customer personas and segmentation to deliver personalized experiences throughout the customer lifecycle.

Minimum Qualifications:

Education: Bachelor’s degree (or equivalent experience) in Analytics, Computer Science, Statistics, Mathematics, Economics, or a related highly quantitative field. (Master's degree preferred).
Experience: 3+ years of directly applicable experience in data science, predictive modeling, and building end-to-end data pipelines within a marketing or customer acquisition context.
Technical Proficiency:
- Advanced proficiency in (Python with deep experience in data modeling packages.
- Advanced SQL skills and experience manipulating high-dimensional consumer datasets.
- Proficiency using Vertex AI/Colab notebooks.
Domain Knowledge: Solid understanding of digital marketing channels (Paid Search, Paid Social, Programmatic), web analytics platforms (e.g., Google Analytics, Adobe Analytics), and MarTech/AdTech ecosystems.
Ambiguity Management: Proven ability to navigate dynamic, fast-paced, and entrepreneurial environments with ambiguous or evolving project requirements.
Familiarity with Customer Data Platforms (CDP) in supporting no code/low code Marketer driven target audiences.

Communication Skills: