Senior Data Scientist

Senior Data Scientist II
Onsite in Cincinnati, Ohio (Downtown 5 Days Onsite)
12 months Contract

Experience Level: 2 10+ Years
Employment Type: Contract / Consulting Opportunity

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.

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 Google Cloud Platform
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 Google Cloud Platform.
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
Google Cloud Platform
Machine Learning
Deep Learning
Recommender Systems
Personalization Engines
A/B Testing
MLOps

What Success Looks Like
Successful candidates will:
Deliver high-performing recommendation models that improve customer engagement and conversion.
Develop scalable personalization solutions serving millions of customers.
Improve recommendation quality, diversity, and relevancy metrics.
Collaborate effectively across Data Science, Engineering, Product, and Business teams.
Contribute to the long-term evolution of personalization and loyalty strategy initiatives.