About this job
We’re looking for a Data Scientist who wants to mold the Wonderkind product to their insights. Where your actions to improve campaign and ad performance will play a key role in the intelligence of our product. Laying the groundwork for what our development team develops and our programmatic team tests. You’ll analyze the incoming data across all our clients, work with the development team as a product owner on data related aspects of the tool (ie. Analytics dashboard & reporting pages). All to support our mission how to get the best match between an applicant and our customers.
Who do you work with?
You work with a variety of great multicultural people. You’ll be part of the Product team consisting of developers, product owners, designers and other internal stakeholders like Customer Success and Sales
What’s the job about?
The core of the role is offering learning to our product and customers on what works and what does not around the advertising of Employer Brands or Jobs.
- Delivering performance-enhancing projects and take a lead on projects around targetting and profiling.
- Define and create customer value from our data together with Product & Customers setting up dashboards & learning features.
- Visit/talk to customers at least 2 times each month brainstorming on product development sessions and discuss how customers are using data.
- Analyze Wonderkind and Social data to support advice and learning on the best campaign set up and be an integral part of how development builds this into the platform.
- Work together with the Engineers to set up a future proof data structure.
- Define and achieve the Product Roadmap from a data science perspective, and assist in data engineering if possible.
- Be passionate about solving problems using data.
- Capable of implementing advanced statistical techniques and concepts (e.g. hypothesis testing, resampling techniques, Bayesian inference) in real-world business cases
- A comprehensive understanding of Python, SQL, and relational databases
- Knowledge of a variety of machine learning techniques (regression, classification, clustering, computer vision), algorithms (Logistic regression, SVM, gradient boosting, neural networks) and their real-world advantages/drawbacks.
- Experienced with data wrangling (Numpy, Pandas) and experience optimizing algorithms and machine learning models using open source libraries (Scikit-learn, Tensorflow) and evaluating their performance.
- A drive to stay on the cutting edge by mastering new technologies and techniques.
Does this make you excited? Apply Now!
Acquisition not appreciated