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 :

- 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 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.