At a Glance
- Tasks: Lead a data science team to tackle complex business challenges with innovative solutions.
- Company: Join HelloFresh, a global leader in food technology with a vibrant culture.
- Benefits: Enjoy flexible working, personal growth opportunities, and discounts on meals.
- Other info: Work in a hybrid setting with access to continuous learning and development.
- Why this job: Make a real impact in a dynamic environment while mentoring future data scientists.
- Qualifications: Experience in leading data science teams and strong skills in Python and machine learning.
The predicted salary is between 121500 - 148500 $ per year.
Job Description
As the Staff Data Scientist, you will lead and develop a high-performing data science team, translating complex business challenges into scalable analytical and machine learning solutions that drive measurable commercial outcomes across Hello Fresh's brands.
This role is based in Chippendale, Sydney (hybrid working environment) and reporting to the Director Data ANZ.
- You will…
- Build and develop the data science team by providing technical mentorship, regular coaching, and stretch opportunities.
- Partner with senior stakeholders across commercial, product, and operations to shape the data science roadmap and communicate progress and outcomes clearly.
- Drive delivery of high-impact projects including ML models, experimentation frameworks, and analytical solutions.
- Maintain technical depth by contributing to hands‑on work, including reviewing code, validating modelling approaches, and ensuring technical rigour.
- Champion best practices in experimentation (A/B testing), model development, and deployment.
- Manage and prioritise the team's roadmap, balancing strategic initiatives with BAU demands.
- Recruit and retain data science talent, contributing to a collaborative culture.
- You have…
- Demonstrated experience leading or managing data science teams with a track record of developing talent and delivering end‑to‑end projects.
- Strong hands‑on background in Python and Py Spark, with experience in Databricks or equivalent cloud‑based data platforms.
- Experience with traditional data science tools and techniques, such as XGBoost and deep learning frameworks.
- Deep knowledge of A/B testing methodology and machine learning model development, validation, and production deployment.
- Prior experience as a practicing data scientist capable of engaging credibly at a technical level.
- You are…
- An influential partner capable of translating ambiguous business problems into clear data science briefs for senior stakeholders.
- A mentor who values curiosity, technical rigour, and continuous improvement within a team.
- Driven by commercial impact, retention, acquisition, and operational efficiency.
- Familiar with deep learning frameworks and their practical business application (desirable).
- What you’ll get in return…
- The chance to have a significant impact in a leading global food technology company
- Flexible working arrangements
- Egoless environment, constant learning with room for personal growth
- Free access to Headspace
- Hello Fresh Academies - internal learning & development
- A diverse and vibrant international environment, with offices in 18 countries
- Company discount - 70% off Hello Fresh & 40% off Youfoodz
Start Date
Immediately available - Can wait a notice period
Duration
Permanent full time role
Location
Chippendale Head Office - hybrid working environment
Salary
- Competitive salary & employee benefits
- #J-18808-Ljbffr
Staff Data Scientist in Sydney employer: HelloFresh
As a People Specialist at our Chippendale office, you'll be part of a dynamic team within a leading global food technology company that values collaboration and continuous improvement. We offer flexible working arrangements, a vibrant international culture, and significant opportunities for personal growth through our HelloFresh Academies. With competitive salaries and generous employee discounts, we are committed to creating a rewarding and engaging workplace for all our employees.
