Principal Data Scientist - Consumer

United States Digital Space LLC; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical/Dental/Vision Insurance

401(k) Retirement Savings Plan

HSA or FSA eligibility

Long and Short-Term Disability

Fitness Reimbursement Program

25% employee discount & FAM Membership

Flexible PTO

Group Life Insurance

EAP through AllOne Health

Job summary

the company is seeking a Principal Data Scientist, Consumer to lead personalization models for search, home feed, carts, and marketing at scale. You will design, implement, and ship recommender systems and ranking strategies while guiding agentic AI initiatives.

You will mentor data scientists, collaborate with product and engineering leaders, and drive experimentation to improve customer experience and business outcomes.

Qualifications

• 10+ years of experience in data science or ML.
• Track record shipping recommender/ranking/personalization systems.
• Deep knowledge of gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, causal methods.
• Hands-on experience with agentic AI systems using LLMs and retrieval.
• Expert Python and core ML stack (pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow).
• Strong SQL and Snowflake experience; data warehousing proficiency.
• Clear communication with executives and non-technical partners.

Responsibilities

• Own consumer personalization end to end across home feed, search, pages, cart, and CRM.
• Build recommenders and rankers; design candidate generation and learning-to-rank systems.
• Lead agentic AI for consumers with LLM-powered agents and tools.
• Blend ML and LLMs; decide tool choix and production integration.
• Run rigorous experiments; design AB tests and evaluation frameworks.
• Ship to production with feature pipelines, latency budgets, and monitoring.
• Mentor senior data scientists and shape team standards.
• Shape the roadmap with Product and Engineering leaders.

Skills

Recommender systems

Learning to rank

Agentic AI

Python

SQL

Communication to executives

Tools

Snowflake

Pytorch / TensorFlow

scikit-learn

Job description

the company delivers everyday essentials in minutes from our own network of micro-fulfillment centers. Every session, a customer sees a small, fast-changing assortment that depends on where they are, what's in stock, and what they need right now. Getting that experience right is one of our biggest levers for growth.

As Principal Data Scientist, Consumer, you will be the technical lead for how the company personalizes the shopping experience. You will design and ship the recommendation, ranking, and personalization models behind search, browse, carts, and marketing, and you will lead our work on agentic AI experiences for consumers. You will set technical direction, mentor data scientists, and partner closely with Product, Engineering, and Marketing leaders.

What We Offer

• Medical/Dental/Vision Insurance
• 401(k) Retirement Savings Plan
• HSA or FSA eligibility
• Long and Short-Term Disability Insurance
• Fitness Reimbursement Program
• 25% employee discount & FAM Membership
• Flexible PTO
• Group Life Insurance
• EAP through AllOne Health (formerly Carebridge)

What You'll Do

Own consumer personalization end to end. Define the modeling strategy for recommendations, ranking, and personalization across the home feed, search, product pages, cart, and CRM.Build recommenders and rankers. Design candidate generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and real-time inventory availability.Lead agentic AI for consumers. Build LLM-powered agents that help customers plan, discover, and reorder (for example, turning "taco night for six" into a ready cart), including tool use, retrieval, evaluation, and guardrails.Blend classic ML and LLMs. Decide when a gradient-boosted model, a two-tower network, or an LLM is the right tool, and combine them in production systems.Run rigorous experiments. Design A/B tests and offline evaluation frameworks, choose the right metrics, and connect model gains to customer and business outcomes.Ship to production. Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality.Set the bar. Mentor senior and staff data scientists, lead design reviews, and raise standards for modeling, code quality, and measurement across the team.Shape the roadmap. Work with Product and Engineering leaders to choose the problems with the highest impact and explain trade-offs clearly to executives.

What You'll Bring

10+ years of experience in data science or machine learning, or 8+ years with a PhD in a quantitative field (computer science, statistics, operations research, or similar).A track record of shipping recommendation, ranking, or personalization systems that measurably moved consumer metrics at scale.Deep knowledge of classic machine learning: gradient boosting, collaborative filtering, matrix factorization, learning-to-rank, embeddings, and causal and experimental methods.Hands-on experience building agentic AI systems with LLMs, including prompt and tool design, retrieval-augmented generation, multi-step agents, and evaluation of agent quality and safety.Expert Python skills and fluency with the core ML stack (for example pandas, scikit-learn, XGBoost or LightGBM, PyTorch or TensorFlow).Strong SQL and experience working with large data warehouses; hands-on experience with Snowflake.Comfortable using AI coding assistants such as Claude to build models and pipelines faster, with the judgment to review, test, and validate AI-generated code and to protect customer data.Solid grounding in A/B testing, offline-to-online metric alignment, and statistical inference.Experience leading technical direction across teams without direct authority, and mentoring senior data scientists.Clear communication with both technical and non-technical partners, including executives.

Nice to Have

Experience with Databricks or a similar platform (Spark, MLflow, feature stores) for large-scale training and model management.Background in e-commerce, grocery, quick commerce, or other marketplaces where inventory and location shape what customers can buy.Experience with real-time or session-based recommendations, contextual bandits, or reinforcement learning.Familiarity with agent frameworks and LLM evaluation tooling, and with fine-tuning or distilling models for cost and latency.Experience with dbt, Airflow, or similar tools for data pipelines.Publications, patents, or open-source work in recommender systems, information retrieval, or applied LLMs.

Compensation

• the company pays employees based on market pricing and pay may vary depending on your location. The salary range below reflects what we’d reasonably expect to pay candidates. A candidate’s starting pay will be determined based on job-related skills, experience, qualifications, interview performance, and market conditions. These ranges may be modified in the future. Exceptions may be made for exceptional individuals.
• This role is eligible for a discretionary annual cash bonus and participation in the company’s equity incentive plan.
• Remote Base Salary Range: $180,000 - $240,000

CompensationAdditional InformationAt the company, we know that life can be unpredictable. Sometimes you forget the milk at the store, run out of pet food for Fido, or just really need ice cream at 11 pm. We get it—stuff happens. But that’s where we come in, delivering all your wants and needs in just minutes.

And now, we’re assembling a team of motivated people to help us drive forward that vision to bring a new age of convenience and predictability to an unpredictable world.

Like what you’re hearing? Then join us on Team Blue.

#LI-the company

the company is an equal employment opportunity employer, committed to an inclusive workplace where we do not discriminate on the basis of race, sex, gender, national origin, religion, sexual orientation, gender identity, marital or familial status, age, ancestry, disability, genetic information, or any other characteristic protected by applicable laws. We believe in diversity and encourage any qualified individual to apply.