Applied Science- Growth
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Do NOT repeat the job title as a heading. So we need sections: About the role (maybe from "About The Role" paragraph). Responsibilities (from "What You'll Do"). Requirements (maybe from "What You'll Need"?). Qualifications (maybe advanced degree etc). Skills (maybe Python, etc). Benefits, Pay, Schedule: not present, so omit.
We must preserve all substantive info, not invent. Remove boilerplate like cookie/navigation/legal etc (none). Also remove duplicated lines.
We need to parse the content.
Let's extract sections:
About Opendoor: description of company mission. Could be considered "About the role"? Actually "About Opendoor" is company overview. The instruction says use headings like About the role, Responsibilities, etc. There's no explicit "About the role" heading but there is a paragraph "About The Role". So we can put that under About the role
. The "About Opendoor" could be considered part of that? Might be separate but we can include it as part of About the role or maybe as a separate section? The instruction only lists those headings; we should not create other headings. So we should incorporate the company description into About the role section (maybe as a paragraph). Or we could omit it? But we must preserve ALL substantive information. So we need to include it somewhere. Since we cannot create a custom heading, we can put it inside the About the role section as a
. That seems fine. Now Responsibilities: from "What You'll Do" bullet-like sentences. We'll make a
- list.
Requirements: from "What You'll Need" maybe includes required skills and qualifications. Could split into Requirements and Qualifications and Skills. But we need to decide.
The posting lists:
"What You'll Need
Strong Python skills, with experience building maintainable software and contributing to production ML systems.
Experience taking predictive models from problem definition and training through deployment, evaluation, and iteration, with a strong foundation in classification and statistical modeling.
Applied experience in causal inference and experimental design, including estimating incremental effects and reasoning about confounding, selection bias, and uncertainty.
Ability to evaluate models using both predictive performance and the business outcomes of the decisions they inform.
Comfort working with imperfect data, delayed outcomes, and ambiguous business questions, and translating findings into clear recommendations for technical and business partners.
An advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or a related quantitative field, or equivalent applied research experience."
Then "Nice to Have" list.
We could treat "What You'll Need" as Requirements (mandatory). The advanced degree could be Qualifications. Skills could be Python, etc. But we can also combine.
Given instruction: Prefer a few substantive sections over many thin ones. So maybe we combine Requirements and Qualifications into one section? But they ask to use headings like Requirements, Qualifications, Skills. We could include all three if they have content. Let's see:
- Requirements: maybe the bullet points about experience, skills, ability.
- Qualifications: advanced degree.
- Skills: Strong Python skills, experience building maintainable software, etc. But that overlaps.
Better to follow typical structure: Requirements (mandatory experience), Qualifications (education), Skills (technical). We'll extract.
From "What You'll Need": we can assign:
Requirements:
- Experience building maintainable software and contributing to production ML systems.
- Experience taking predictive models from problem definition through deployment, evaluation, iteration.
- Applied experience in causal inference and experimental design.
- Ability to evaluate models using predictive performance and business outcomes.
- Comfort working with imperfect data, delayed outcomes, ambiguous business questions, translating findings into clear recommendations.
Qualifications:
- Advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or related quantitative field, or equivalent applied research experience.
Skills:
- Strong Python skills.
- Foundation in classification and statistical modeling.
- Experience with causal inference and experimental design (maybe duplicate).
But we can keep skills as: Strong Python skills, experience building maintainable software, experience taking predictive models through lifecycle, applied causal inference, ability to evaluate models, comfort with imperfect data.
But that duplicates requirements. Might be okay.
Alternatively, we could just have one section "Requirements" containing all the "What You'll Need" and "Nice to Have" as optional? But we need to follow headings.
Given the instruction to prefer few substantive sections, maybe we can combine Requirements and Qualifications into one section called "Requirements" and include education there. And maybe Skills separate? But we have to decide.
Let's examine the content: The "Nice to Have" list includes:
- Experience in media targeting and measurement, including marketing mix modeling (MMM), multi-touch attribution (MTA), and budget allocation.
- Experience in customer acquisition, lifecycle marketing, and personalization, including customer lifetime value and next-best-action systems.
- Background in real estate, housing, and other marketplaces with long customer decision cycles.
- Familiarity with distributed data processing, such as PySpark.
These could be considered "Preferred Qualifications" or "Nice to Have". Could be placed under "Qualifications" or "Skills". Since we have a Qualifications heading, we could put the advanced degree there, and maybe also include nice-to-have as part of Qualifications? But better to keep Qualifications for mandatory education, and maybe add a separate "Preferred Qualifications" but not in list. Since not allowed, we could omit nice-to-have or incorporate into Skills as optional.
But we must preserve ALL substantive information. So we need to include nice-to-have somewhere. We could add a section "Skills" and include both required and nice-to-have skills there. Or we could add a subsection under Requirements? But we must use
- for any list of duties/requirements/benefits. So we can have multiple lists.
Thus we can have:
About the role
... company description + role description ...
Responsibilities
- ...
Requirements
- ... mandatory requirements ...
Qualifications
- ... advanced degree ...
Skills
- ... technical skills (Python, etc) ...
. About The Role paragraph: "About The Role We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor. This role will focus primarily on machine learning, causal inference, optimization, and decision-making under uncertainty, with applications spanning marketing investment, customer acquisition, lifecycle engagement, and conversion. This role will contribute to our broader growth ecosystem, and we’re looking for someone who can combine strong modeling intuition with hands-on execution and strong engineering to build practical solutions for a low-margin, high-stakes business where small improvements in acquisition efficiency and customer conversion can have an outsized impact. You’ll work on problems like predicting seller intent and conversion, estimating customer lifetime value, building marketing mix models, and developing optimizers that help us allocate spend and identify which customer interactions drive incremental growth. We’re a small, nimble team, so there’s ample opportunity to shape both the modeling direction and how these systems get used in production decision-making." We'll include that as part of About the role section maybe after company description. Now Responsibilities: "What You'll Do" bullet-like sentences: - Build models and decision systems that support profitable growth through better customer acquisition and engagement. - Develop intent, conversion, and lifetime value models to optimize acquisition and customer engagement. - Build and improve marketing measurement and optimization systems that guide budgeting and the allocation of marketing spend across channels, markets, and time. - Apply causal inference to experimental and observational data to estimate incremental marketing impact and how it varies across customers and markets. - Partner with Marketing, Product, Engineering, and Sales to turn models into systems that influence real decisions. - Bring a pragmatic, hands-on approach: move quickly from research and prototyping to production, and own ongoing model evaluation and improvement. We'll make each a
- . Now Requirements: from "What You'll Need" mandatory items (maybe all bullet points except advanced degree?). Let's list: - Strong Python skills, with experience building maintainable software and contributing to production ML systems. - Experience taking predictive models from problem definition and training through deployment, evaluation, and iteration, with a strong foundation in classification and statistical modeling. - Applied experience in causal inference and experimental design, including estimating incremental effects and reasoning about confounding, selection bias, and uncertainty. - Ability to evaluate models using both predictive performance and the business outcomes of the decisions they inform. - Comfort working with imperfect data, delayed outcomes, and ambiguous business questions, and translating findings into clear recommendations for technical and business partners. We'll put these as
- .
Qualifications: advanced degree:
- An advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or a related quantitative field, or equivalent applied research experience.
Skills: maybe we can list technical skills like Python, etc. But we already have Python in requirements. To avoid duplication, we could treat Skills as "Nice to have" plus maybe some additional like familiarity with PySpark, etc. Let's extract nice-to-have:
- Experience in media targeting and measurement, including marketing mix modeling (MMM), multi-touch attribution (MTA), and budget allocation.
- Experience in customer acquisition, lifecycle marketing, and personalization, including customer lifetime value and next-best-action systems.
- Background in real estate, housing, and other marketplaces with long customer decision cycles.
- Familiarity with distributed data processing, such as PySpark.
We'll put these under Skills.
Now we need to ensure we don't include any boilerplate like "About Opendoor" heading. We'll just have paragraphs.
Now output HTML fragment only.
Let's craft:
About the role
About Opendoor At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.
About The Role We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor. This role will focus primarily on machine learning, causal inference, optimization, and decision-making under uncertainty, with applications spanning marketing investment, customer acquisition, lifecycle engagement, and conversion. This role will contribute to our broader growth ecosystem, and we’re looking for someone who can combine strong modeling intuition with hands-on execution and strong engineering to build practical solutions for a low-margin, high
- for any list of duties/requirements/benefits. So we can have multiple lists.
Thus we can have: