Applied Scientist, Demand Forecasting presso HelloFresh
HelloFresh · New York, Stati Uniti d'America · Onsite
- Professional
- Ufficio in New York
Applied Scientist, Demand Forecasting - Job Description:
As an Applied Scientist on the Demand Forecasting team, you will help shape how HelloFresh predicts and plans customer demand across our US supply chain. Your work will sit at the intersection of data science, optimization, and operations. You’ll be building models that directly drive business-critical decisions from menu planning and procurement to fulfillment.
You’ll partner closely with product, engineering, and operations teams to design, test, and deploy advanced forecasting and decision systems. You’ll explore and apply state-of-the-art machine learning and statistical techniques to improve forecast accuracy, quantify uncertainty, and enable better planning decisions. Beyond building models, you’ll play a key role in translating complex scientific insights into scalable tools and strategies that enhance efficiency and reduce waste throughout the supply chain.
You will…
- Develop, prototype, and deploy forecasting models that capture complex demand patterns and constraints across multiple brands, products, and time horizons.
Build scalable data pipelines leveraging Databricks, Snowflake, and AWS.
- Create mathematical optimization models and tools to solve practical business problems; roll them out.
- Collaborate cross-functionally with procurement, planning, and operations teams to translate scientific work into measurable business outcomes.
- Identify problems proactively, formulate and implement robust and data-driven solutions.
- Mentor and support analysts, engineers, and scientists within the team. Share best practices in modeling, and scientific rigor to elevate overall team technical excellence.
You are...
- A hands-on problem solver with experience applying scientific methods to real-world operational challenges.
- Skilled in Python and familiar with common machine learning and optimization techniques.
- Experienced in data manipulation and analytics within cloud-based ecosystems.
- Agile – you thrive in fast-paced and dynamic environments and are comfortable working autonomously
- Comfortable balancing scientific rigor with business pragmatism; able to communicate insights effectively to both technical and non-technical audiences.
- Detail-oriented – you possess strong organizational skills and consistently demonstrate a methodical approach to all your work.
You have…
- A degree in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Operations Research, or a related discipline (Master’s or PhD preferred).
- 3+ years of experience applying machine learning, forecasting, or statistical modeling in an applied business setting.
- 2+ years of experience building tools/applications in a high-level language, such as Python or Java.
- Experience working with large, complex datasets using SQL and Python-based data ecosystems.
- Familiarity with operations and supply chain management concepts - forecasting, planning, optimization, logistics experience preferred.
You’ll get…
- Competitive hourly rate, 401K company match that vests immediately upon participation, & team bonus opportunities
- Generous PTO and flexible attendance policy
- Comprehensive health and wellness benefits with options at $0 monthly, effective first day of employment
- Up to 85% discount on subscriptions to HelloFresh meal plans (HelloFresh, Green Chef, Everyplate, and Factor_)
- Access to Employee Resource Groups that are open to all employees, including those pertaining to BIPOC, women, veterans, parents, and LGBTQ+
- Inclusive, collaborative, and dynamic work environment within a fast-paced, mission-driven company that is disrupting the traditional food supply chain
This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.