Senior Data Scientist

Overview

Seeking a Senior Data Scientist to join a high-impact Personalization & Loyalty Strategy team supporting one of the largest e-commerce organizations in the United States. This team powers trillions of recommendation decisions annually and delivers highly personalized experiences to millions of customers.

This role is focused on designing and building next-generation recommender systems, personalization engines, and deep learning models that influence product discovery, coupon recommendations, substitute recommendations, and shoppable recipe experiences.

The ideal candidate brings hands-on experience developing large-scale recommendation systems, deep learning expertise, and a passion for turning customer behavior data into meaningful business outcomes.

Location: Cincinnati, OH (Downtown – 5 Days Onsite)

Experience Level: 2–10+ Years

Employment Type: Contract / Consulting Opportunity

Top Skills Required

Must Have

Recommender Systems / Personalization Experience

Deep Learning Model Development

TensorFlow or PyTorch

Python

SQL

Apache Spark

Machine Learning Model Evaluation

Experiment Design / A-B Testing

Statistical Analysis

Customer Personalization

Preferred

Databricks

Azure or GCP

MLOps

Data Engineering

Retail / E-Commerce Experience

Search Relevancy Systems

Customer Analytics

What You'll Do

As a member of the Relevancy Team, you will build and optimize recommendation engines that improve customer engagement and drive revenue growth through personalized experiences.

You will work alongside data scientists, machine learning engineers, software engineers, data engineers, product managers, and business stakeholders to design, train, evaluate, deploy, and continuously improve recommendation systems operating at enterprise scale.

This role offers the opportunity to solve complex machine learning challenges involving customer behavior, product affinity, loyalty engagement, and personalization strategies.

Key Responsibilities

Recommender Systems Development

Design, build, and optimize recommendation engines for e-commerce personalization.

Develop deep learning models for product recommendations, coupon recommendations, substitute recommendations, and recipe recommendations.

Research and implement advanced recommendation algorithms including:

Collaborative Filtering

Matrix Factorization

Deep Learning Recommenders

Sequence Models

Embedding-Based Approaches

Hybrid Recommendation Systems

Model Evaluation & Optimization

Define evaluation frameworks and success metrics.

Perform offline model evaluation and online experimentation.

Conduct A/B testing to compare recommendation strategies.

Analyze recommendation quality, diversity, and customer engagement metrics.

Perform root cause analysis to improve recommendation accuracy and relevance.

Personalization & Customer Analytics

Incorporate customer preferences, shopping behavior, engagement history, and loyalty data into recommendation models.

Improve personalization experiences using transactional, demographic, behavioral, and product data.

Develop strategies that balance recommendation relevance with recommendation diversity.

Production & Deployment Support

Partner with ML Engineers to support:

Model deployment

Model serving

Model monitoring

Model versioning

Production pipelines

Contribute to MLOps and operationalization best practices.

Analytics & Reporting

Build customer analytics datasets and performance dashboards.

Develop reporting solutions to monitor recommendation effectiveness.

Generate actionable insights for business stakeholders.

Collaboration & Knowledge Sharing

Collaborate closely with Data Science, Engineering, Product, and Business teams.

Document technical approaches, findings, and best practices.

Contribute reusable tools, libraries, and internal frameworks.

Participate in technical mentoring and knowledge-sharing sessions.

Required Qualifications

2+ years of experience building large-scale recommender systems.

Experience developing deep learning models for personalization use cases.

Strong proficiency with TensorFlow or PyTorch.

Strong programming skills in Python.

Advanced SQL proficiency.

Experience using Apache Spark for large-scale data processing.

Strong understanding of:

Statistics

Experimental Design

Hypothesis Testing

Exploratory Data Analysis

Machine Learning Evaluation Metrics

Experience working in cloud environments such as Azure or GCP.

Strong communication and presentation skills.

Ability to work independently and take ownership of initiatives.

Excellent analytical and problem-solving skills.

Preferred Qualifications

Experience with Databricks.

Experience supporting production ML systems.

MLOps experience.

Data Engineering experience.

Retail, grocery, loyalty, or e-commerce experience.

Search relevance and ranking experience.

Experience working with large-scale customer behavior datasets.

Technical Environment

Python

SQL

Apache Spark

TensorFlow

PyTorch

Databricks

Azure

GCP

Machine Learning

Deep Learning

Recommender Systems

Personalization Engines

A/B Testing

MLOps