Staff Data Scientist, Planning and Forecasting

Другое

• ABOUT Company 
• Founded in 2018, Company was built to challenge the idea that nice things have to cost a lot. Our mission is simple: to make really high quality essentials for really low prices, produced fairly and sustainably. We believe everyone deserves exceptional craftsmanship and timeless design without the traditional markups. Company is a direct-to-consumer (DTC) model that cuts out middlemen and leverages just-in-time manufacturing to minimize waste and maximize value.
• Company is a tech company disrupting the retail industry by putting AI, analytics and automation at the center of everything we do. Our unwavering commitment to excellence and company values guide our teams and actions:
• Customer First : We prioritize customer satisfaction in every decision.
• High Quality : True quality means premium materials and rigorous production standards you can feel good about.
• Essential Design : We focus on timeless, functional essentials instead of chasing trends.
• Always a Better Deal : Innovation and transparency ensure value for both customers and partners.
• Social & Environmental Responsibility : We commit to sustainable materials, ethical production, and fair wages.
• Company partners with world-class manufacturers across the globe and serves millions of customers. With strong investor backing and a focus on sustainable growth, we are a company that is rapidly scaling while maintaining a commitment to quality, simplicity, and radical price transparency.
• OUR TEAM AND SUCCESS 
• At Company, you will be part of a high-performing team that is redefining what quality, value, and sustainability mean in modern retail. We are a destination for builders, innovators, and operators to come together and challenge the status quo. Our collective ambition is bold. We are creating an entirely new category and customer experience – one that democratizes luxury and provides high quality products at radically low prices. That mission demands a world-class team committed to excellence. 
• If you are motivated by impact, growth, and purpose, you will find a strong sense of belonging at Company.
• THE ROLE
• Staff Data Scientist, Planning and Forecasting
• Company is building its own supply chain planning science capability from scratch. This includes demand forecasting at multiple geographic scales, methodology-agnostic forecasting tournaments, inventory placement optimization across a growing international network, vendor performance modelling, and the raw material signal generation that links the forecast back to procurement before failures happen.
• The Staff Data Scientist sets the science charter and writes the roadmap, driving its load-bearing components end-to-end. You’ll be the deep specialist on a broad mandate (the forecasting tournament implementation, the inventory placement model, the vendor performance system) with full ownership of the methodology, the production model, and the iteration loop.
• You’ll work closely with charter leadership, mentor the DS3s and DS2s on the team, and partner directly with planning operators.
• We expect AI-native science. The methodology you bring should already include LLM-aided exploratory work, agentic feature engineering, and AI-augmented experimentation. Your standards for what counts as a real result should be high enough that AI assistance accelerates rather than dilutes them. The ideal candidate has roughly a decade of applied data science or operations research experience, with deep expertise in one or two domains relevant to supply chain planning. They’ve owned modelling workstreams end-to-end across multiple companies or products, and they have the craft and the patience to take a hard problem and stay with it until the model actually moves the metric.
• They are excellent at being given an ambiguous problem and solving it exceptionally well. They mentor junior scientists; they earn trust with operators; they argue for the right methodology even when it’s the harder one to implement.
• They are AI-native in their science workflow as a matter of course. They use LLMs in EDA and feature work, run agentic loops where they make sense, evaluate AI-driven models on equal footing in a tournament framework, and have the rigor to keep AI assistance from quietly degrading the science.
• Responsibilities
• Own the science workstreams end-to-end: the forecasting tournament implementation, the inventory placement optimization, the vendor performance system, or the raw material signal pipeline - across methodology, production model, and iteration loop
• Hold the methodological standard for your area: when to use which model class, what constitutes a defensible evaluation, what to do when the data is too sparse or too noisy
• Partner with the Planning Tools engineering team on what your workstream needs from the platform, and on the constraints production places back on what you can build
• Bring depth across statistical, ML, and AI-driven methods; evaluate them on their merits within a tournament framework rather than advocating any one school
• Set the standard for experimentation discipline within the science team: clean splits, honest backtests, the willingness to reject your own hypothesis
• Drive AI-native science workflow (LLM-aided EDA, agentic feature discovery, AI-augmented experiment design) with rigor to match
• Mentor scientists within the team; raise the methodological floor of the people around you through code review, design discussion, and direct teaching
• Partner with planning operators on the problems within your workstream; translate their operational reality into well-defined modelling problems, and your model outputs into decisions they can act on
• Hold the methodological line in business conversations: educate operators on what your models can and can’t support, and push back on misclassified signals or over-fitted requests
• Qualifications
• Required:
• 8+ years of applied data science or operations research experience, with deep expertise in one or two domains relevant to supply chain planning
• Demonstrated ownership of modelling workstreams end-to-end; from problem framing through production deployment, iteration, and measured business impact
• Real methodological breadth across forecasting and OR: classical, ML, and AI-driven approaches, with informed opinions about which to reach for
• Engineering fluency to own your work end-to-end: feature pipelines, experiments, model serving
• AI-native science practice you can speak to in detail with examples of where AI tooling materially changed how you do science, and where you held your standards against it
• Track record of mentorship: scientists who became better because they worked with you
• Preferred: 
• Advanced degree in a quantitative field (Statistics, CS, Operations Research, Engineering, Economics) preferred; PhD a plus
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• All posted ranges are reflective of base salary and may vary depending upon experience level and location. Bonus and equity may also be provided for eligible roles.
• WHY Company?
• Joining Company means being part of a mission-driven team reshaping retail. You will work alongside talented colleagues, tackle meaningful challenges, and contribute to building a more sustainable, accessible future for customers and partners alike.
• EQUAL OPPORTUNITY & HIRING INTEGRITY
• Company provides equal employment opportunities to all employees and applications for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. 
• Company is committed to providing reasonable accommodations to qualified individuals with disabilities. If you need a reasonable accommodation to complete your application or to perform the essential functions of a role at Company, please let us know by completing this accommodation form . We review all requests individually and will work with you to determine appropriate accommodations on a case-by-case basis.
• Employment is contingent upon successful completion of a background check. Company will conduct background checks in compliance with applicable federal, state, and local laws.
• Security Advisory: Beware of Frauds
• At Company, we're dedicated to recruiting top talent who share our drive for innovation. To safeguard candidates, Company emphasizes legitimate recruitment practices. Initial communication is primarily via official Company email addresses and LinkedIn; beware of deviations. Personal data and sensitive information will not be solicited during the application phase. Interviews are conducted via phone, in person, or through the approved platforms Google Meets or Zoom—never via messaging apps or other calling services. Offers are merit-based, communicated verbally, and followed up in writing. If personal information is requested to initiate the hiring process, rest assured it will be through secure and protected means.
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