
Silicon Stack
10 hours ago
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 user-friendliness.
Key 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 development.
Required Skills & Experience :
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 skills.
Preferred 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.
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