Senior Data Scientist

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Innovative Information Technologies, Inc, is seeking the following. Apply via Dice today! 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.