Data Scientist Level 2

Senior Data Scientist, Recommender Systems (Relevancy / Personalization & Loyalty Strategy)

LeadStack Inc. is an award winning, one of the nation's fastest growing, certified minority owned (MBE) staffing services provider of contingent workforce. As a recognized industry leader in contingent workforce solutions and Certified as a Great Place to Work, we're proud to partner with some of the most admired Fortune 500 brands in the world.

Location: Cincinnati, OH, Onsite

Duration: 12+ months

Pay Rate: $70/hr – $90/hr (W2)

Role overview: The Relevancy/Personalization team builds large-scale personalized customer experiences for a major e-commerce site, delivering recommendations at scale to millions of customers. The team covers product and coupon recommender systems, substitute recommendations, and shoppable recipes. We seek a talented and experienced senior data scientist specialized in building search and recommender systems, with a track record developing deep learning models, expertise in ML frameworks, and strong understanding of recommendation techniques.

Responsibilities:

• Design, develop, and implement recommender systems tailored to retail and e-commerce personalization needs.
• Build advanced machine learning and deep learning models for personalized product, coupon, substitute, and recipe recommendations.
• Define evaluation methods and key metrics to measure recommender performance and identify improvements.
• Conduct A/B testing and offline model evaluations to compare recommendation strategies.
• Perform root cause analysis and model interpretability reviews to understand and improve results.
• Improve personalization by incorporating customer preferences, dietary needs, shopping behaviors, and engagement signals.
• Explore recommendation diversity strategies to expose customers to a broader range of relevant products while maintaining accuracy.
• Partner with ML engineers to support model deployment, serving, versioning, and production pipeline best practices.
• Collaborate with data scientists, data engineers, full-stack engineers, product teams, and business stakeholders.
• Integrate transactional, customer, product, demographic, and user feedback data for model development and analytics.
• Build customer analytics pipelines, reporting dashboards, and performance tracking to monitor recommendation effectiveness.
• Document best practices, technical insights, lessons learned, and contribute to internal tools and libraries.
• Participate in knowledge-sharing sessions and technical discussions.

Required skills / Must-have qualifications:

• 2+ years proven experience building deep learning models for large-scale recommender systems.
• Recommender systems/personalization experience is required.
• Partnering or familiarity on model deployment & MLOps should be sufficient.
• Azure/Databricks/Spark is strongly preferred, but demonstrated experience with GCP is acceptable.
• Proficiency with ML frameworks such as TensorFlow or PyTorch.
• Proficiency in SQL, Python, and Spark for data analysis and manipulation.
• Strong statistics, design of experiments, exploratory data analysis, and insights generation.
• Experience with cloud platforms (Azure or GCP).
• Experience collaborating with Data Engineering and MLOps for model deployment.
• Strong problem-solving, analytical, and communication skills; ability to convey complex ideas to technical and non-technical stakeholders.