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Description:
Haus is a decision science platform focused on the new digital privacy paradigm where data sharing and PII is restricted.
The company utilizes causal inference based econometric models to run experiments that help brands understand the impact of their marketing, pricing, and promotions on their bottom line.
The team consists of former product managers, economists, and engineers from notable companies like Google, Netflix, Amazon, and Meta.
The mission is to make high-quality decision science tooling and incrementality testing accessible to all businesses.
The role involves building the Causal Marketing Mixed Model (cMMM), a machine learning-driven engine for optimizing advertising investments across various channels.
The position is for both Senior and Staff level engineers to develop robust data onboarding and automated data ingestion pipelines for the cMMM.
Responsibilities include partnering with product and science teams, designing and maintaining data pipelines, onboarding data types, resolving data discrepancies, and mentoring team engineers.
Requirements:
Candidates must have 4-7+ years of experience as a Software Engineer.
Experience in building and deploying products that rely on large-scale distributed systems using Python, Go, Scala, Java, or similar languages is required.
Proficiency in third-party integrations and APIs is essential.
Experience with cloud infrastructure such as Google Cloud or AWS is necessary.
Familiarity with SQL databases is required.
Bonus points for earlier stage startup experience, knowledge of build systems and infrastructure management tools like Terraform, experience with data warehouses (Snowflake, BigQuery, RedShift), and data workflow orchestration tools (Dagster, Airflow).
A BS, MS, or PhD in Computer Science, Applied Mathematics, or a related field is preferred.
Benefits:
The position offers a competitive salary and startup equity.
Employees receive top-tier health, dental, and vision insurance.
A 401k plan is provided.
The company supplies the necessary tools and resources for productivity, including new laptops and equipment.
Employees will be part of a small team that has a significant impact on the overall output of the company.