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Senior Data Scientist - Recommender Systems (P508)

8451 · 6 days ago
Cincinnati, OH; Chicago, IL; Deerfield, IL
Negotiable
Full-time
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84.51° Overview:

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

Join us at 84.51°!

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G2 – Senior Data Scientist, Relevancy Team – Personalization & Loyalty Strategy (P508)

Relevancy Team is responsible for making relevant and personalized customer experiences for Kroger's E-commerce site, which ranks among the top 10 ecommerce companies in the US. We deliver trillions of recommendations to the Kroger website at scale and make them available to millions of Kroger 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. The ideal candidate will have proven track record of developing deep learning models, expertise in ML frameworks such as TensorFlow or PyTorch, and a strong understanding of various recommendation models and techniques.

What does the role entail? (Responsibilities)

Develop innovative recommender systems. Design, develop and implement different recommender systems tailored to the unique needs and challenges of grocery retail industry. Utilize advanced machine learning, including deep learning models, to create personalized recommendations that enhance customer satisfaction and drive business growth.

Evaluate and improve recommendation performance. Establish rigorous evaluation methodologies to assess the performance of recommendation algorithms across key metrics. Conduct A/B testing and offline evaluations to compare the effectiveness of different recommendation strategies and iterate on model improvements based on empirical results. Conduct root cause analysis and model interpretability studies to understand the factors driving recommendation outcomes and identify opportunities for model improvements.

Enhance personalization and diversity. Enhance the level of personalization in recommendation algorithms to better reflect individual user preferences, dietary restrictions, and shopping habits. Explore techniques for diversifying recommendations to expose users to a broader range of products and promote impulse buying while maintaining relevancy and accuracy.

Model serving and deployment. Coordinate with ML engineers in the deployment of recommender system models by incorporating robust deployment pipelines, and best practices for model serving and versioning. Utilize containerization technologies such as Docker to package and deploy models efficiently, facilitating reproducibility and scalability in production environment.

Collaborate with cross-functional teams. Collaborate closely with other data scientists, data engineers, and full stack engineers to implement data science solutions. Collaborate with product management and business leads to understand business objectives, share actionable insights, and drive further optimization efforts.

Analytics and insights generation. Integrate diverse sources of data, including transactional data, customer demographics, product attributes and user feedback to build datasets for model development, driving generation of actionable insights and analytics to inform the development and evaluation of recommender systems. Develop customer analytics pipelines and reporting dashboards to track key performance metrics, monitor user engagement metrics and assess the effectiveness of recommendation strategies over time.

Document and knowledge sharing. Document best practices, lessons learned, technical insights related to model development and inference for internal reference and knowledge sharing. Contribute to the development of internal tools, libraries, and documentation resources to streamline adoption and maintenance of recommender system solutions within organizations. Actively participate in knowledge sharing sessions and tech talks to disseminate expertise and foster a culture of continuous learning and development.

What skills and experience do you need? (Requirements)

  • Bachelor’s/Master’s degree or equivalent in computer science, data science, statistics, mathematics, analytics, or related discipline.
  • 2+ 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. Experience working with Databricks is a plus.
  • Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation.
  • Experience working with cloud platforms like Azure or GCP.
  • Experience working with Data Engineering and MLOps is desirable.
  • 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.
  • Prior experience in the retail or e-commerce industry is a plus.
  • 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.

 

Why join our team? (Rewards)

Impact millions of people. As a member of our data science team, you will have the opportunity to make a tangible difference in the lives of millions of customers by delivering relevant and personalized recommendations that enhance their grocery shopping experience. Your work will directly contribute to increasing customer satisfaction and loyalty, driving business outcomes for our company.

Continuous learning and development. Challenge yourself. We are committed to fostering a culture of continuous learning and development. You will have access to resources and support for expanding your knowledge and skills in cutting-edge technologies, including recommender systems, machine learning and artificial intelligence. Our team encourages exploration and experimentation, providing opportunities to stay at the forefront of industry advancements.

Work on new developments in recommender systems. Join a team at the forefront of innovation in recommender systems and AI. You will have the chance to contribute to pushing the boundaries of what’s possible in personalized recommendation technology. You will have the chance to work on exciting projects that leverage the latest developments in deep learning architectures and data science methodologies.

 

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Last updated on Jun 10, 2024

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