Data Scientist
hace 21 horas
Edison
About Wakefern \n Wakefern Food Corp. is the largest retailer-owned cooperative in the United States and supports its co-operative members’ retail operations, trading under the ShopRite®, Price Rite®, The Fresh Grocer®, Dearborn Markets®, Fairway®, Di Bruno Bros. and Gourmet Garage® banners. \n \n Employing an innovative approach to wholesale business services, Wakefern focuses on helping the independent retailer compete in a big business world. Providing the tools entrepreneurs need to stay a step ahead of the competition, Wakefern’s co-operative members benefit from the company’s extensive portfolio of services, including innovative technology, private label development, and best-in-class procurement practices. \n \n About You \n Summary \n This is a, “full-stack” data role for a rare builder who can both engineer the data pipeline—ingestion, transformation, and modeling—and apply advanced statistics, machine learning, and applied AI on top of it to put analysis directly into the hands of merchants, planners, buyers, and supply chain partners. You will own your work end to end: not just the model, but the data that feeds it and the tool someone opens to use it. \n \n Just as important, you can speak the language of the business. You move fluidly between deeply technical work and plain business terms, translating a P&L or merchandising question into an analytical solution and the result back into a decision. This is not a reporting or dashboard-maintenance role—you do not simply consume data that others prepare. You architect, transform, and productionize the data, then turn it into models, insights, and applications people actually use. \n This is the right role for someone who wants range rather than a narrow specialty, and who would rather ship something imperfect that gets used than perfect something that does not. \n \n Why This Role Matters \n Wakefern is making a significant, multi-year investment in data science and AI. We are rebuilding our data and AI capability on modern cloud technology—including Palantir Foundry and AIP—and reacting to customer trends by shifting merchandising strategy toward hyper-personalized offers built on first-party loyalty data. The team will also apply data science, optimization, and applied AI to the supply chain that moves product to hundreds of stores every day. \n \n This team’s output will influence pricing, promotions, assortment, sourcing, forecasting, inventory, and logistics across the largest retailer-owned cooperative in the United States. \n \n We are building this function new. There is no legacy codebase to inherit and no established way of doing things to work around. The people who join now will choose the tools, set the standards, and define what good looks like. \n \n What You Will Work On \n\n • Personalization and loyalty: propensity, next-best-offer, customer lifetime value, and churn models built on first-party shopper data, so offers are targeted to individual households rather than to the whole store.\n, • Pricing and promotion: price and promotion elasticity, promotional lift and incrementality measurement, and offer optimization to support the transition from hi-lo to personalized promotions.\n, • Merchandising: assortment optimization, market basket and affinity analysis, category performance, space and planogram analytics, and private label opportunity sizing.\n, • Procurement and sourcing: cost and deal analytics, vendor performance and lead time variability, forecast accuracy, and supply assurance modeling that strengthens negotiating position and protects service levels.\n, • Supply chain and logistics: demand forecasting at store, DC, and item level; inventory, safety stock, and replenishment optimization; warehouse slotting, space, and cube utilization; labor planning and productivity; transportation, load building, and route optimization; on-shelf availability and fresh shrink reduction.\n, • Customer sentiment, intent, and trend signals: social listening and external signal data from platforms such as Reddit, TikTok, X, and review sites, through licensed tools or purpose-built connectors—sentiment and emotion classification, topic modeling, entity extraction, and emerging trend detection used to inform new item selection, private label development, assortment, and campaign response.\n, • Applied AI and agentic workflows: LLM-based applications built on a shared semantic model / ontology in Palantir Foundry and AIP—including retrieval over internal documents and contracts, structured extraction from invoices and vendor paperwork, natural language interfaces to operational data, and agents that draft or execute routine decisions under human oversight. Built with real evaluation, not demos.\n, • Platform and data products: reusable pipelines, well-modeled ontology-backed data products, and decision applications (e.g., Foundry Workshop) that other teams build on top of rather than rebuild.\n\n \n Core Functions \n\n • Build, validate, and deploy models and analyses end to end, owning the full lifecycle from data ingestion and feature engineering through deployment, monitoring, and retraining (MLOps).\n, • Design, build, and maintain data pipelines and transformations (ETL/ELT) in our cloud data platform—including Palantir Foundry—along with the data-modeling and data-quality checks that keep them trustworthy and reusable.\n, • Write clean, tested, version-controlled Python and SQL, and take part in code review as both author and reviewer, applying software-engineering discipline (Git, testing, CI/CD).\n, • Design and analyze experiments and holdout tests, apply causal inference, and report results honestly—including negative ones—so decisions rest on real impact rather than correlation.\n, • Build decision-support applications and, where appropriate, executive-ready visualizations, and support the stakeholders who use them.\n, • Partner with business stakeholders to understand context and constraints, ask the questions that surface what they actually need, and explain the “so what” in plain business language.\n, • Contribute to LLM-based features and their evaluation (on Foundry/AIP or any other modern data platform) where they are the right tool for the problem.\n, • Work with unstructured and third-party data—customer reviews, social listening feeds, and vendor documents—alongside transactional data.\n, • Document your work so that someone else can pick it up, extend it, and trust it.\n, • Invest in learning the business. Time in stores, distribution centers, and with category teams is part of the job.\n\n \n Additional Functions \n\n • Challenge assumptions and the status quo with evidence, including when the answer is not the one the business expected.\n, • Contribute to team standards for code quality, model documentation, reproducibility, and ongoing model monitoring.\n, • Adjust priorities across multiple concurrent projects while handling ad hoc requests, without letting long-horizon work stall.\n, • Stay current on retail analytics, machine learning, optimization, and generative AI, and pilot promising approaches where they create measurable value.\n\n \n Knowledge and Skill Requirements \n Required \n\n • Bachelor’s degree in a quantitative or technical field such as Data Science, Statistics, Computer Science, Engineering, Industrial Engineering, Operations Research, Economics, or Mathematics, or equivalent practical experience.\n, • Demonstrable, hands-on experience spanning both data engineering (building production pipelines) and data science / machine learning, or a strong portfolio of substantive technical project work. Candidates whose experience is limited to reporting on or visualizing data prepared by others will not meet this bar.\n, • Working proficiency in Python (pandas, NumPy, scikit-learn) and SQL, with genuine comfort writing production-quality code rather than only reading it.\n, • Foundation in statistics and machine learning, including regression, classification, clustering, and time series forecasting.\n, • Understanding of experimental design and why control groups matter.\n, • Business acumen and the ability to explain technical work to people who do not share your background.\n, • Appetite to learn quickly. Much of this work has no established playbook at Wakefern yet.\n\n \n Technical Skills We Value \n\n • Platform breadth: comfort working across the full stack of a modern data platform—from ingestion and transformation through modeling, deployment, and the application layer. We value people who learn new platforms quickly over people who know one deeply.\n, • Palantir Foundry & AIP: hands-on experience (or the ability to ramp quickly) with pipeline building (Pipeline Builder / Code Repositories), ontology and semantic data modeling, and building operational and AI-driven applications (Workshop, AIP Logic, and agent workflows). This is a core part of our current stack.\n, • Data engineering: PySpark or other distributed processing, dimensional and semantic data modeling, incremental and batch pipeline design, orchestration, data quality frameworks, and integration of third-party and social APIs, including rate-limited and streaming sources.\n, • Software development: Git-based workflows, code review, unit testing, CI/CD, packaging and dependency management, and API design. TypeScript or JavaScript for building front-end applications is a plus.\n, • Forecasting at scale: hierarchical and intermittent demand forecasting, promotional and new item forecasting, and forecast value add measurement.\n, • Natural language processing: text classification, sentiment and emotion analysis, topic modeling, named entity recognition, and embeddings, using libraries such as spaCy, Hugging Face transformers, NLTK, or gensim. Experience with social listening or review data through tools such as Brandwatch, Sprinklr, or Talkwalker is a plus.\n, • Optimization and operations research: linear and mixed-integer programming, constraint solvers, heuristics, and simulation. Several of our highest-value supply chain problems are optimization problems, not prediction problems.\n, • Cloud data platforms: Palantir Foundry, Google Cloud (BigQuery, Vertex AI, Dataflow), Databricks, Snowflake, AWS, or comparable.\n, • Visualization and application delivery: Foundry Workshop, Tableau, Power BI, Looker, Streamlit, or comparable tools for putting analysis in front of decision makers.\n, • AI engineering: building production applications on large language models, including retrieval-augmented generation, tool use and agentic workflows, structured extraction, prompt and context engineering, and guardrails.\n, • AI evaluation and observability: building evaluation sets, offline and online testing, human review loops, and monitoring of quality, cost, and latency. We treat an AI feature without an evaluation harness as unfinished.\n\n \n Preferred \n\n • Grocery, retail, wholesale, CPG, or e-commerce experience, particularly in loyalty, personalization, pricing, promotions, assortment, forecasting, inventory, or distribution and transport.\n, • Familiarity with syndicated, panel, and loyalty data sources such as Circana/IRI, Nielsen, Numerator, dunnhumby, or 84.51.\n, • Exposure to supply chain systems and their data, such as WMS, TMS, replenishment and forecasting engines, labor management, or space and planogram tools.\n, • Experience building recommendation systems, uplift models, or personalization engines used by real customers.\n, • Applied NLP experience on customer, review, or social data, including sentiment, intent, or trend detection.\n, • Advanced degree in a quantitative or technical discipline.\n\n \n Working Conditions & Physical Demands \n\n • Availability to work a varied, flexible schedule to meet business demands\n, • Ability to monitor computer screens for long periods of time\n, • Ability to work a hybrid schedule as established by the division (4 days on-site, 1 day remote)\n\n \n Core Competencies \n\n • Communicate Effectively: Communicates thoughts and ideas in a well-organized manner, encouraging two-way communication.\n, • Build Relationships: Creates cross-functional partnerships through the development and maintenance of constructive and cooperative relationships.\n, • Stay Competitive: Demonstrates a mindset of continuous improvement while exhibiting passion and enthusiasm for their work.\n, • Embrace Change: Looks for new ways of working by supporting advancements in processes and technology.\n, • Develop You: Identifies and capitalizes on opportunities for personal and professional career growth.\n, • Drive for Results: Supports divisional and strategic objectives through achievement of work goals.\n\n \n Compensation and Benefits \n The salary range for this position is $100,000 — $125,000 annually. Placement in the range depends on several factors, including experience, skills, education, geography, and budget considerations. \n \n Wakefern is proud to offer a comprehensive benefits package designed to support the health, well-being, and professional development of our Associates. Benefits include medical, dental, and vision coverage, life and disability insurance, a 401(k) retirement plan with company match, paid time off, holidays, and parental leave. Associates also enjoy access to wellness and family support programs, fitness reimbursement, educational and training opportunities through our corporate university, and a collaborative, team-oriented work environment. Many of these benefits are fully or partially funded by the company, with some subject to eligibility requirements. \n \n About The Work \n This job description is intended to set forth the general nature and essential duties and responsibilities of the work performed by associates for this position. It may not contain a comprehensive inventory of all duties, responsibilities, and qualifications required of associates to do this job. Associates may be assigned additional responsibilities as necessary. The Company reserves the right to revise this job description at a
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