Role: Data Scientist – Search, Recommendations & Personalization
Location: Cincinnati, OH (Local Preferred; Open to Strong Remote Candidates)
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Duration: 12-month contract, with a high likelihood of extension/conversion
Requirement Description:
Position Details:
Position Overview:
Isoft is seeking a highly experienced Data Scientist to join its Search, Recommendations & Personalization team. This team is responsible for building intelligent, customer-facing machine learning solutions that improve how users discover products, promotions, coupons, deals, and content across digital channels.
The ideal candidate brings deep expertise in developing and deploying recommendation systems, search relevance models, ranking algorithms, and personalization solutions in production environments. You will work closely with Product Owners and Machine Learning Engineers to transform business challenges into scalable machine learning solutions that drive engagement, conversion, and customer satisfaction.
This is an opportunity to contribute to a highly visible initiative focused on creating next-generation digital customer experiences through advanced machine learning, experimentation, and AI-driven innovation. The role aligns with broader enterprise efforts supporting search, recommendation, ranking, and personalization platforms.
Key Responsibilities:
Design, develop, and optimize machine learning models that power personalization, recommendations, and search experiences.
Build and enhance recommendation systems for products, promotions, coupons, deals, and content.
Develop features and analytical datasets from large-scale customer, transaction, and behavioral data.
Perform feature engineering, statistical analysis, model evaluation, and performance optimization.
Design and execute A/B tests and other experimentation frameworks to measure model effectiveness and business impact.
Partner with Product Owners to translate business objectives into scalable machine learning solutions.
Collaborate closely with Machine Learning Engineers to operationalize, deploy, and scale models in production environments.
Explore opportunities to leverage Agentic AI workflows and emerging AI technologies where they create measurable business value.
Communicate technical findings, recommendations, and insights to both technical and non-technical stakeholders.
Drive improvements in customer engagement, personalization, and product discovery experiences.
Required Qualifications:
8+ years of professional experience in Data Science, Machine Learning, Applied Science, or a related discipline.
Proven experience building production recommendation systems, personalization platforms, ranking models, search relevance systems, or similar customer-facing machine learning products.
Strong expertise in:
Python
Apache Spark
SQL
Machine Learning
Statistical Modeling
Experience with:
Strong experience with feature engineering and model optimization.
Experience designing experiments and evaluating model performance through A/B testing and related methodologies.
Ability to translate ambiguous business challenges into measurable analytical solutions.
Demonstrated success operating in complex environments with minimal direction.
Strong communication, stakeholder management, and cross-functional collaboration skills.
Preferred Qualifications:
Experience within retail, grocery, eCommerce, marketplace, or consumer technology organizations.
Experience developing recommendation, search, personalization, or ranking systems at scale.
Familiarity with modern GenAI and LLM-powered development workflows.
Knowledge of Agentic AI concepts and frameworks.
Hands-on experience with:
Technical Environment:
Must-Have Technologies
Python
SQL
Apache Spark
Databricks
Microsoft Azure
Machine Learning
Statistical Modeling
Feature Engineering
A/B Testing
Recommendation Systems
Nice-to-Have Technologies
Agentic AI
LangChain
LangGraph
What Success Looks Like
In this role, you will help shape the future of search, recommendation, and personalization capabilities by leveraging large-scale behavioral and transaction data to deliver intelligent customer experiences. Successful candidates will demonstrate strong ownership, thrive in ambiguity, and effectively partner across product, engineering, and business teams to build impactful machine learning solutions.
Ideal Background:
Candidates with experience in:
Recommendation Systems
Search Relevance
Ranking Models
Personalization Platforms
Customer Analytics
eCommerce & Retail Machine Learning
Consumer-Facing AI/ML Products
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