Data Scientist

Hey there, we're Crew. We're the exclusive talent partners for some of Australia's most impactful and innovative technology businesses.  Like

Comprehending customer behaviour and its potential implications poses a substantial challenge for numerous B2C enterprises. The unexplored possibilities residing within customer data are virtually boundless, and the Sourse products empower customers to effectively uncover and leverage these opportunities.

The Sourse Decision Augmentation Platform serves as a dedicated Data Science team for clients, granting them access to hitherto undiscovered opportunities.

This role is all about working as a part of our data science team analysing client data, developing new analytical features for our machine learning models and developing churn, retention, upsell and cross-sell models for new customers. 

Your primary responsibilities will involve:

• Analysing and modelling customer tabular data, predominantly using SQL, Jupyter notebooks, and Kubeflow pipelines for model deployment. 
• Complete end-to-end model delivery process, starting from data validation and offline feature and model development, all the way to delivering production-ready feature sets and model artifacts.
• Building upon the standardised feature repository to assess the applicability of existing features and create new ones for our clients. 
• Contribute to developing tools that automate data analysis, data issue identification, dataset creation, and feature selection for model training and testing.
• Helping our customer success team understand Telco subscriber behaviour based on interpretable propensity models to design marketing campaigns

The business has developed industry-specific universal data models, which provide standardised feature sets based on data from various companies. This enables them to develop and maintain a reusable feature repository, prediction pipelines, and training those pipelines for the machine learning models. 

This approach allows the team to leverage existing modelling code and extend it in a reusable manner for training and evaluating machine learning models on medium to large commercial datasets. Your models will focus on predicting churn, upsell, cross-sell, and retention. Additionally, you'll contribute to the development of tools that automate data analysis, data issue identification, dataset creation, and feature selection for model training and testing.

The core skills and competencies required to be successful in this role are:

• This role is ideal for an early to mid career professional with at least 3 years of professional experience in modelling tabular data, extra points if you have at least 1 year in the Telco industry or experience working with marketing campaigns based on propensity models.
• Strong programming skills in Python.
• Solid theoretical knowledge and hands-on experience with machine learning and feature engineering.
• Advanced proficiency in SQL, aggregations over time for example
• Solid understanding of statistics.
• Experience in a cloud environment, preferably Google Cloud.

Why Join?:

• Be part of a rapidly growing AI startup with a global presence and exciting expansion plans.
• Limited hierarchy, layers of approval and policy, meaning you can get things done.
• Be included in the bonus scheme.
• Learn and thrive alongside a talented crew of software engineers and data scientists, some even wielding PhDs.
• Contribute to cutting-edge technologies that help our clients to make better decisions, and therefore better businesses.
• Enjoy a warm and collaborative work atmosphere where your ideas are welcomed and valued.

If you are an independent, self-directed, detail-oriented individual with a strong commitment to quality and are interested in collaborating with a dynamic team to work on innovative projects, we encourage you to get in touch with us. We look forward to hearing from you.

Think you might apply? Here's some tips; 

• Feel free to contact me directly if you have any questions you'd like to ask prior to formally applying. I'm here to help.
• Cover letters. There's no need to add one, but if you write one i'll read it. But please dont just replicate details from your resume, if you're going to the trouble of writing one make it insightful.
• We understand that not every candidate will meet every single desired qualification. If your experience looks a little different but you genuinely feel that you have something to offer, we'd love to hear from you.
• Our hiring process will involve an initial phone call to discuss the role and how your skills and experience might fit. From there it's 2 separate interviews with the team, the first one being more general about the role and some high level technical dialogue, the 2nd one will be a 60 minute data science task on a dataset. You'll be asked to analyse and model the data in python using a Jupyter notebook.