Senior Applied Data Scientist, 1:1 Personalization, Audience and Experimentation

Target

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Brooklyn Park, MN
Salary
$98,000–$176,000 / yr
Employment
Full-time
Posted
6 days ago
Freshness
Confirmed live today
Closes
Nov 20, 2026
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $210k
This role $137k
$78k $289k
below market most similar roles pay here above market

This role pays less than 86% of similar roles. Most pay $166,125–$253,500 — the blue band above. At the midpoint, this role pays about $137k versus about $210k for comparable roles.

Based on 240 similar postings.

Employer

About Target

Target Corporation is a large-format general merchandise and grocery retailer offering a wide assortment of everyday essentials, apparel, home goods, and electronics through stores and online. Industry: General Merchandise Retail

Target currently has 92 open roles on FindRole.

Listed pay typically runs $98,000–$176,000 across 67 roles with salary data.

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View all roles at Target

At a glance

TL;DR · Senior Applied Data Scientist, 1:1 Personalization, Audience and Experimentation

Role Posting Log in to save

JOB TITLE: Sr Applied Data Scientist - 1:1 Personalization, Audience and Experimentation (applied ML, PyToch, A/B testing) As a Sr Applied Data Scientist within the Data Sciences team, you will develop machine learning models to power one-to-one personalization across marketing and digital channels. You will design, build, and deploy advanced models including recommendation systems, ranking models, customer propensity models, and reinforcement learning approaches. Your daily work involves collaborating with cross-functional partners to translate business problems into data science solutions and leading experimentation using A/B testing, multi-armed bandits, and causal inference. You will utilize Python, SQL, PyTorch, TensorFlow, and Scikit-learn to build production-ready systems. The role requires expertise in NLP, deep learning, and large-scale data platforms like GCP Vertex AI, Spark, Databricks, and Snowflake to solve complex personalization and measurement problems at scale.

Skills

Python SQL TensorFlow PyTorch Scikit-learn GCP Vertex AI Spark Databricks Snowflake Machine Learning Deep Learning NLP Reinforcement Learning A/B Testing Causal Inference Transformers Large Language Models Embeddings Recommendation Systems

What you'll do

  • Design, build, and optimize machine learning models for one-to-one personalization, including recommendation systems and ranking models.
  • Develop testing frameworks using A/B testing, multi-armed bandits, and causal inference to measure personalization impact.
  • Translate complex business problems into technical data science solutions in partnership with product and engineering teams.
  • Ensure machine learning models are production-ready, scalable, and integrated into digital platforms with high reliability.
  • Evaluate and apply advanced AI technologies like deep learning, transformers, and reinforcement learning to personalization use cases.
  • Build and deploy highly performant code for model performance at scale across marketing and digital channels.

What we're looking for

  • 4-year degree in quantitative disciplines (Science, Technology, Engineering, Mathematics, Statistics) or equivalent industry experience.
  • 3+ years of applied data sciences experience, end to end with digital marketing and measurement teams.
  • Strong hands-on experience in personalization/recommendations, applied ML, NLP, and deep learning.
  • Experience with statistical tools of A/B testing at the customer level and matched–market testing.
  • Experience with real-time systems, online learning, and recommendation algorithms at scale.
  • Demonstrated proficiency in Python, SQL, and modern ML libraries such as TensorFlow, PyTorch, and Scikit-learn.
  • Experience with large-scale data platforms including GCP Vertex AI, Spark, Databricks, and Snowflake.
  • Proven track record of building and deploying applied ML models in production environments.

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