Silicon Stack - Data Scientist - Python / ETL

Silicon Stack is a leading Australian software development and consulting company headquartered in Melbourne, serving a global clientele across Australia, New Zealand, Europe, North America, UK, and Asia.

Renowned for our "can-do attitude" and proven track record, we deliver high-quality solutions with expertise in cutting-edge technologies.

We excel in functionality, aesthetics, and Responsibilities :

• Design and implement causal inference and incrementality models, such as randomized holdouts, synthetic control, uplift regression, and time-series forecasting.
• Develop profitability and ROI models at the SKU, product, and customer levels applicable to retail media, CRM campaigns, and financial forecasting.
• Automate and maintain robust data pipelines using SQL, Python, and R to unify data across systems like Amazon Ads, Salesforce, Adobe Analytics, SAP, and CRM databases.
• Conduct ad-hoc statistical analyses and simulations, including A / B testing, media mix modeling, and customer journey analytics.
• Partner with Finance, Marketing, and Operations teams to translate analytical insights into strategic business recommendations.
• Mentor junior analysts, ensuring the use of rigorous statistical methodology and scalable model Skills & Experience :
• 5 to 7+ years of experience in Data Science or Advanced Analytics.
• Proficient in Python and R, including libraries such as statsmodels, scikit-learn, Prophet, and causal inference packages.
• Advanced SQL skills for data wrangling, query optimization, and ETL workflows.
• Hands-on experience with incrementality testing, campaign attribution, and ROI modeling (e.g., for retail media or CRM).
• Familiarity with marketing and enterprise tools : Adobe Analytics, Salesforce (SFDC), SFMC, SAP, Google Ads, Meta Ads.
• Strong business acumen in profitability analysis, marketing ROI, and P&L impact.
• Exceptional communication and stakeholder engagement Qualifications :
• Experience working with large-scale marketing datasets and customer lifecycle analytics.
• Exposure to cloud platforms and data engineering tools (e.g., AWS, GCP, Airflow, dbt).
• Understanding of privacy-first analytics and data governance in marketing tech stacks.

(ref : hirist.tech)