Data Scientist

About Alloy At Alloy, we work with consumer goods companies that make the products we eat, wear, and use every day, as well as the ones we occasionally splurge on. We’re tackling a real and complex problem for them—managing supply and demand in the face of constantly changing customer behavior, highly complex supply chain networks, 40-year-old data standards and labor-intensive manual processes. Alloy is a fast-growing, well-funded startup with expanding offices across the world. Our team hails from successful startups, leading tech companies and Fortune 100 enterprises. We believe deeply in fostering individual ownership, iterating to excellence, focusing on what matters, communicating openly & respectfully, and supporting one another.  We encourage people of all backgrounds to apply. Alloy is committed to creating an inclusive culture, and we celebrate diversity of all kinds. About the role As a data scientist at Alloy, you will be at the forefront of helping our customers forecast their true demand. The forecasts you build will lead directly to real world decisions, like how much product is manufactured and where it is shipped to. Accurate forecasts drive short-term replenishment decisions, form the backbone of demand planning, and inform portfolio and sales strategies - all key parts for operating a modern supply chain efficiently and sustainably! Together with the forecasting team, you will own forecasting end-to-end. The team's ownership ranges from building and maintaining our forecasting models and pipeline to measuring the performance of the resulting predictions all the way to the frontend. Your main focus will be data science, but there is also an opportunity to grow beyond that role and venture into frontend and backend development. You will provide thought leadership for forecasting best practices and standards both internally and with customers. You're comfortable with ownership - together with the team, you will help define the vision and roadmap for the forecasting efforts at Alloy. Our data engineering team takes care of wrangling and storing the supply chain data among all the different partners our customers work with - you will have terabytes of readily usable, clean data at your disposal. We're looking for self-starters who aren't afraid to get their hands dirty. We’ve grown our staff significantly in the past year, and as a rapidly expanding startup, there’s plenty of opportunity to make an impact and develop your career. Our engineering culture emphasizes code reviews, mentorship, and collaboration. We foster individual ownership of engineering work across the team and lean on each others' strengths when needed. We strive to write clear, extensible code that your colleagues love to read and keep the bug backlog in check. We focus heavily on promoting best practices for code quality, automation, and testing. Technologies we use Python 3, R, Pandas, Numpy, Prophet, Java 16 , PostgreSQL, TypeScript, jOOQ, Dropwizard, AutoValue, Guava, React, Redux, Redis, SASS, Jest, Cypress, Google Cloud Platform, BigQuery, BigTable, PubSub, Kubernetes (GKE), Terraform, Containers (e.g. Docker). Our values Focus on what matters: Reevaluate priorities as necessary to pick the work that will have the highest impact to the customer and business. Work hard in a way that is sustainable. Choose the schedule that lets you achieve your best. Measure results, not hours. Respect your time and that of others; come prepared and leave with commitments. Take ownership: Take initiative by proactively identifying opportunities or issues and tackling them. Strive for excellence. Hold yourself and your colleagues to a high standard. Create something that makes you proud. Follow through on our commitments to customers, users, and each other. We are all doers, regardless of role — no task is beneath you. Iterate to excellence: Prefer action over perfection to learn quickly from early feedback. Be flexible and accept the need to make tradeoffs and change directions. Everything is a work in progress, nothing is done. Question prevailing assumptions, but understand them first. Gather evidence and champion ideas that will make our products and our company better. Verify and validate. Communicate openly and respectfully: Offer and expect transparency to build trust. Confirm mutual understanding, especially in the face of disagreement. Treat each other with respect. Criticize constructively — the work, not the person. Have each other’s back: When interacting with each other, always assume good intentions. Trust each other and take risks together. Give praise generously and take joy in others’ accomplishments. Grow a diverse team by actively seeking different backgrounds and cultivating an inclusive culture. Focus on how others’ strengths compliment us, rather than how their weaknesses invalidate them.