Data Scientist
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive information, not invent or summarize away detail. So we need to include all points from the posting.
Let's extract content:
Intro paragraph: "We’re reinventing the market research industry. Let’s reinvent it together. At Numerator, we believe tomorrow’s success starts with today’s market intelligence. We empower the world’s leading brands and retailers with unmatched insights into consumer behavior and the influencers that drive it. Numerator’s Data Science team provides statistical and methodological leadership across the organization, developing the methodologies, tools, and data products that support our products. This is a wide-ranging applied data science role focused primarily on the consumer purchase panel underlying our largest products, with opportunities to contribute to other complex data science initiatives across Numerator’s broader product portfolio. The work combines applied statistics, data investigation, methodology development, and practical implementation. Many of the questions we tackle do not have a single observable right answer, requiring us to combine statistical evidence, product context, and subject-matter judgment to understand tradeoffs and determine whether a solution improves the product as a whole. As your expertise grows, you will take on increasingly complex work and have opportunities to deepen your technical expertise, lead projects, improve team practices, and shape how we work."
That's About the role.
Responsibilities (How You'll Spend Your Time):
- Design and implement statistical methodologies that improve the representativeness, stability, and quality of Numerator’s products.
- Develop data science solutions to problems involving consumer behavior, panel composition, and data quality using techniques such as sampling, weighting, statistical modeling, classification, predictive modeling, anomaly detection, and optimization.
- Build scalable Python and SQL solutions, internal tools, and data products that turn statistical methodologies into reliable production workflows.
- Develop metrics, monitoring, and validation frameworks to evaluate system performance and identify unexpected or unintended behavior.
- Contribute throughout the project lifecycle, from problem definition and exploration through implementation, testing, deployment, and ongoing evaluation.
- Work closely with Product, Engineering, Data Operations, and business partners to shape requirements and develop solutions that are both statistically sound and practical to use.
- Communicate methodologies, findings, assumptions, and tradeoffs clearly to technical and non-technical audiences.
Requirements/Qualifications (What You'll Bring to Numerator):
- A bachelor’s or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience.
- Two to five years of industry experience developing and delivering data science solutions.
- A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems.
- Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets.
- Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners.
- Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required.
- Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub.
- Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes.
Benefits (What We Offer):
- An inclusive and collaborative company culture—we work in an open environment while working together to get things done and adapt to changing needs as they arise.
- A market-competitive total compensation package.
- Volunteer time off and charitable donation matching.
- Strong support for career growth, including mentorship programs, leadership training, access to conferences, and employee resource groups.
- A great benefits package including health, vision, and dental coverage; a Personal Spending Account; unlimited PTO; flexible scheduling; 401(k) matching; travel reimbursement; and more.
We need to avoid filler like "market-competitive total compensation package" maybe considered filler but it's a statement. The rule says never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state "A market-competitive total compensation package." That's a statement about compensation, albeit vague. It's not a specific number but it's still a statement. The rule about filler mentions "commensurate with experience", "not specified", "N/A", "varies", "TBD". "Market-competitive total compensation package" is not exactly those, but could be considered vague. However it's still a statement about compensation. I'd include it in Benefits section as a bullet.
We must not create Pay section separately; we can include compensation statement within Benefits.
Thus sections: About the role, Responsibilities, Requirements, Benefits.
We need to use
for each heading.
Inside each section, we can have
for prose and
- for lists.
For About the role: it's a paragraph; we can put the whole intro in
.
For Responsibilities: list.
For Requirements: list.
For Benefits: list.
Make sure not to duplicate lines.
Let's craft HTML.
We'll output:
About the role
We’re reinventing the market research industry. Let’s reinvent it together. At Numerator, we believe tomorrow’s success starts with today’s market intelligence. We empower the world’s leading brands and retailers with unmatched insights into consumer behavior and the influencers that drive it. Numerator’s Data Science team provides statistical and methodological leadership across the organization, developing the methodologies, tools, and data products that support our products. This is a wide-ranging applied data science role focused primarily on the consumer purchase panel underlying our largest products, with opportunities to contribute to other complex data science initiatives across Numerator’s broader product portfolio. The work combines applied statistics, data investigation, methodology development, and practical implementation. Many of the questions we tackle do not have a single observable right answer, requiring us to combine statistical evidence, product context, and subject-matter judgment to understand tradeoffs and determine whether a solution improves the product as a whole. As your expertise grows, you will take on increasingly complex work and have opportunities to deepen your technical expertise, lead projects, improve team practices, and shape how we work.
Responsibilities
- Design and implement statistical methodologies that improve the representativeness, stability, and quality of Numerator’s products.
- Develop data science solutions to problems involving consumer behavior, panel composition, and data quality using techniques such as sampling, weighting, statistical modeling, classification, predictive modeling, anomaly detection, and optimization.
- Build scalable Python and SQL solutions, internal tools, and data products that turn statistical methodologies into reliable production workflows.
- Develop metrics, monitoring, and validation frameworks to evaluate system performance and identify unexpected or unintended behavior.
- Contribute throughout the project lifecycle, from problem definition and exploration through implementation, testing, deployment, and ongoing evaluation.
- Work closely with Product, Engineering, Data Operations, and business partners to shape requirements and develop solutions that are both statistically sound and practical to use.
- Communicate methodologies, findings, assumptions, and tradeoffs clearly to technical and non-technical audiences.
Requirements
- A bachelor’s or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience.
- Two to five years of industry experience developing and delivering data science solutions.
- A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems.
- Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets.
- Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners.
- Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required.
- Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub.
- Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes.
Benefits
- An inclusive and collaborative company culture—we work in an open environment while working together to get things done and adapt to changing needs as they arise.
- A market-competitive total compensation package.
for prose. We need to preserve all substantive information, not invent or summarize away detail. So we need to include all points from the posting. Let's extract content: Intro paragraph: "We’re reinventing the market research industry. Let’s reinvent it together. At Numerator, we believe tomorrow’s success starts with today’s market intelligence. We empower the world’s leading brands and retailers with unmatched insights into consumer behavior and the influencers that drive it. Numerator’s Data Science team provides statistical and methodological leadership across the organization, developing the methodologies, tools, and data products that support our products. This is a wide-ranging applied data science role focused primarily on the consumer purchase panel underlying our largest products, with opportunities to contribute to other complex data science initiatives across Numerator’s broader product portfolio. The work combines applied statistics, data investigation, methodology development, and practical implementation. Many of the questions we tackle do not have a single observable right answer, requiring us to combine statistical evidence, product context, and subject-matter judgment to understand tradeoffs and determine whether a solution improves the product as a whole. As your expertise grows, you will take on increasingly complex work and have opportunities to deepen your technical expertise, lead projects, improve team practices, and shape how we work." That's About the role. Responsibilities (How You'll Spend Your Time): - Design and implement statistical methodologies that improve the representativeness, stability, and quality of Numerator’s products. - Develop data science solutions to problems involving consumer behavior, panel composition, and data quality using techniques such as sampling, weighting, statistical modeling, classification, predictive modeling, anomaly detection, and optimization. - Build scalable Python and SQL solutions, internal tools, and data products that turn statistical methodologies into reliable production workflows. - Develop metrics, monitoring, and validation frameworks to evaluate system performance and identify unexpected or unintended behavior. - Contribute throughout the project lifecycle, from problem definition and exploration through implementation, testing, deployment, and ongoing evaluation. - Work closely with Product, Engineering, Data Operations, and business partners to shape requirements and develop solutions that are both statistically sound and practical to use. - Communicate methodologies, findings, assumptions, and tradeoffs clearly to technical and non-technical audiences. Requirements/Qualifications (What You'll Bring to Numerator): - A bachelor’s or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience. - Two to five years of industry experience developing and delivering data science solutions. - A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems. - Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets. - Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners. - Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required. - Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub. - Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes. Benefits (What We Offer): - An inclusive and collaborative company culture—we work in an open environment while working together to get things done and adapt to changing needs as they arise. - A market-competitive total compensation package. - Volunteer time off and charitable donation matching. - Strong support for career growth, including mentorship programs, leadership training, access to conferences, and employee resource groups. - A great benefits package including health, vision, and dental coverage; a Personal Spending Account; unlimited PTO; flexible scheduling; 401(k) matching; travel reimbursement; and more. We need to avoid filler like "market-competitive total compensation package" maybe considered filler but it's a statement. The rule says never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state "A market-competitive total compensation package." That's a statement about compensation, albeit vague. It's not a specific number but it's still a statement. The rule about filler mentions "commensurate with experience", "not specified", "N/A", "varies", "TBD". "Market-competitive total compensation package" is not exactly those, but could be considered vague. However it's still a statement about compensation. I'd include it in Benefits section as a bullet. We must not create Pay section separately; we can include compensation statement within Benefits. Thus sections: About the role, Responsibilities, Requirements, Benefits. We need to use
for each heading. Inside each section, we can have
for prose and
- for lists.
For About the role: it's a paragraph; we can put the whole intro in
. For Responsibilities: list. For Requirements: list. For Benefits: list. Make sure not to duplicate lines. Let's craft HTML. We'll output:
About the role
We’re reinventing the market research industry. Let’s reinvent it together. At Numerator, we believe tomorrow’s success starts with today’s market intelligence. We empower the world’s leading brands and retailers with unmatched insights into consumer behavior and the influencers that drive it. Numerator’s Data Science team provides statistical and methodological leadership across the organization, developing the methodologies, tools, and data products that support our products. This is a wide-ranging applied data science role focused primarily on the consumer purchase panel underlying our largest products, with opportunities to contribute to other complex data science initiatives across Numerator’s broader product portfolio. The work combines applied statistics, data investigation, methodology development, and practical implementation. Many of the questions we tackle do not have a single observable right answer, requiring us to combine statistical evidence, product context, and subject-matter judgment to understand tradeoffs and determine whether a solution improves the product as a whole. As your expertise grows, you will take on increasingly complex work and have opportunities to deepen your technical expertise, lead projects, improve team practices, and shape how we work.
Responsibilities
- Design and implement statistical methodologies that improve the representativeness, stability, and quality of Numerator’s products.
- Develop data science solutions to problems involving consumer behavior, panel composition, and data quality using techniques such as sampling, weighting, statistical modeling, classification, predictive modeling, anomaly detection, and optimization.
- Build scalable Python and SQL solutions, internal tools, and data products that turn statistical methodologies into reliable production workflows.
- Develop metrics, monitoring, and validation frameworks to evaluate system performance and identify unexpected or unintended behavior.
- Contribute throughout the project lifecycle, from problem definition and exploration through implementation, testing, deployment, and ongoing evaluation.
- Work closely with Product, Engineering, Data Operations, and business partners to shape requirements and develop solutions that are both statistically sound and practical to use.
- Communicate methodologies, findings, assumptions, and tradeoffs clearly to technical and non-technical audiences.
Requirements
- A bachelor’s or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience.
- Two to five years of industry experience developing and delivering data science solutions.
- A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems.
- Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets.
- Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners.
- Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required.
- Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub.
- Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes.
Benefits
- An inclusive and collaborative company culture—we work in an open environment while working together to get things done and adapt to changing needs as they arise.
- A market-competitive total compensation package.