Senior Data Scientist

Compunnel, Inc.; Cincinnati, Northern (OH, KY); 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.