Work closely with project manager and internal team members as well as customers on onboarding projects
Use insights from business consultant on customer specific processes to ensure all relevant data is being considered/ transformed in order to find the best fitting models for the customers
End-to-end technical ownership. Being a point of contact for any technical related topics from data engineering data science about the product.
Explain complex data engineering or data science related topics to non-technical stakeholders and customers
Analyze, suggest and prepare AB test setups
Training of mathematical models in AI/ machine learning and statistical learning that describe the effects of prices and promotions on purchasing behaviour, including behavioural price effects.
Ensure the accuracy of the models, development of model validation (back-testing) methods to quantify the impact of the solutions on our clients business.
Collaboration with product management to generate ideas and quickly turn them into efficient, well-tested, working code (R and Python).
Ensure all customer specific time effort is being tracked accurately
Ensure stable data flow incoming and outgoing
Interfacing with customers and external partners to align on data formats and transmission schedules.
Responsible for transforming (normalizing and standardizing) and storing incoming and outgoing data.
Responsible for documenting the solution architecture for each customer
What You Bring
A degree in either computer science, data science, mathematics, statistics, physics, or economics or a comparable field.
Familiarity with relevant topics in mathematics and statistics.
Familiarity with libraries like Numpy, Pandas, Matplotlib, and Statsmodels; PySpark familiarity is a plus.
Good SQL skills.
Excellent communication skills in English, other language is a plus
3+ years of hands-on experience in data analytics, ideally in a retail or consumer products space.
Comfortable analyzing large data sets using tools such as SQL, Python, and R, and are comfortable navigating big data environments.
Experience with customers and stakeholders and ability to explain highly technical data science-related topics in a language suitable for your audience.
