Senior Recommender Systems Scientist - Personalization

Compunnel, Inc. (old); Cincinnati (OH); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Compunnel, Inc. in Cincinnati, OH, is seeking a senior data scientist to lead the design and deployment of search and recommender systems for our e-commerce platform. You will work with cross-functional teams to translate data into impactful recommendations.

The role focuses on deep learning, large-scale data processing, model evaluation, A/B testing, and collaboration with ML engineers to productionize models and monitor performance over time.

Qualifications

• 5+ years of proven experience building deep learning models for large-scale recommender systems.
• Proficiency in ML frameworks such as TensorFlow or PyTorch.
• Proficiency in SQL, Python and Spark for data analysis and manipulation.
• Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation.
• High level of independence to develop and own toolkits, pipelines, and dashboards.
• Excellent problem-solving skills and a proactive approach to addressing challenges.
• Strong analytical and critical thinking skills with attention to detail.
• Must be able to learn from others and teach others and work collaboratively as part of a highly interdependent team.
• Ability to communicate complex ideas effectively to both technical and non-technical stakeholders.

Responsibilities

• Design, develop, and implement recommender systems tailored to grocery retail and e-commerce personalization needs.
• Build advanced machine learning and deep learning models to deliver personalized product, coupon, substitute, and recipe recommendations.
• Define evaluation methods and key metrics to measure recommender system performance and identify areas for improvement.
• Conduct A/B testing and offline model evaluations to compare recommendation strategies and improve model outcomes.
• Perform root cause analysis and model interpretability reviews to understand recommendation results and improve accuracy.
• Improve personalization by incorporating customer preferences, dietary needs, shopping behaviors, and engagement patterns.
• Explore recommendation diversity strategies that 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 to deliver data science solutions.
• Integrate transactional, customer, product, demographic, and user feedback data to support model development and analytics.
• Build customer analytics pipelines, reporting dashboards, and performance tracking to monitor recommendation effectiveness over time.
• Document best practices, technical insights, lessons learned, and model development approaches for internal knowledge sharing.
• Contribute to internal tools, libraries, and documentation that support adoption and maintenance of recommender system solutions.
• Participate in knowledge-sharing sessions and technical discussions to support continuous learning across the team.

Skills

DL models

TensorFlow/PyTorch

SQL

Python

Spark

Statistics

Data analysis

Communication

Tools

Databricks

Azure

GCP

MLops

Job description

JOB SUMMARY
Strategy Relevancy Team is responsible for making relevant and personalized customer experiences for E-commerce site. We deliver trillions of recommendations to the website at scale and make them available to millions of customers. The team has a rich portfolio of sciences which include product and coupon recommender systems, substitute recommendations, and shoppable recipes. We are seeking a talented and experienced senior data scientist to join our data science team, specialized in building search and recommender systems.

Key Responsibilities
Design, develop, and implement recommender systems tailored to grocery retail and e-commerce personalization needs.
Build advanced machine learning and deep learning models to deliver personalized product, coupon, substitute, and recipe recommendations.
Define evaluation methods and key metrics to measure recommender system performance and identify areas for improvement.
Conduct A/B testing and offline model evaluations to compare recommendation strategies and improve model outcomes.
Perform root cause analysis and model interpretability reviews to understand recommendation results and improve accuracy.
Improve personalization by incorporating customer preferences, dietary needs, shopping behaviors, and engagement patterns.
Explore recommendation diversity strategies that 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 to deliver data science solutions.
Integrate transactional, customer, product, demographic, and user feedback data to support model development and analytics.
Build customer analytics pipelines, reporting dashboards, and performance tracking to monitor recommendation effectiveness over time.
Document best practices, technical insights, lessons learned, and model development approaches for internal knowledge sharing.
Contribute to internal tools, libraries, and documentation that support adoption and maintenance of recommender system solutions.
Participate in knowledge-sharing sessions and technical discussions to support continuous learning across the team.

Required Qualifications
5+ years of proven experience building deep learning models for large-scale recommender systems.
Proficiency in ML frameworks such as TensorFlow or PyTorch.
Proficiency in SQL, Python and Spark for data analysis and manipulation.
Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation.
High level of independence to develop and own toolkits, pipelines, and dashboards.
Excellent problem-solving skills and a proactive approach to addressing challenges.
Strong analytical and critical thinking skills with attention to detail.
Must be able to learn from others and teach others and work collaboratively as part of a highly interdependent team.
Ability to communicate complex ideas effectively to both technical and non-technical stakeholders.

Preferred Qualifications
Experience working with Databricks is a plus.
Experience working with cloud platforms like Azure or GCP.
Experience working with Data Engineering and MLOps is desirable.
Prior experience in the retail or e-commerce industry is a plus.