Staff Data Scientist
for section headings. Use - for any list of duties/requirements/benefits, and
for prose.
We need to decide what goes into each section.
About the role: could include location, hybrid, team description, role purpose.
Responsibilities: list each bullet from "Responsibilities" section.
Qualifications: could combine Basic and Preferred? Might split into Basic Qualifications and Preferred Qualifications as subheadings? But only allowed headings are those listed; we could put both under Qualifications heading, with paragraphs separating basic and preferred. Or we could have two sections: Qualifications and Preferred Qualifications? Not in list. Better to keep single Qualifications section with paragraphs.
Skills: from "Suggested Skills": Stakeholder Management, Influencing for Impact, Strategic Thinking. Could be list.
Benefits: mention total compensation package may include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans; link to benefits.
Pay: pay range $149,000 - $245,000.
We must not include filler like "commensurate with experience". Not present.
We must not include duplicated lines.
Let's extract substantive content.
From posting:
Job Description This role is based in Mountain View, CA. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. LinkedIn's Data Science team turns member and customer behavior into product decisions across one of the world's largest professional networks. We create a deep understanding of our members through data, we design experiments, build metric frameworks, and partner with PM, Engineering, and Design to ship features that create real economic opportunity for members — including the next generation of AI-powered experiences. We're hiring a Staff Data Scientist to set technical direction across a product pillar. You'll be the person deciding which questions are worth answering, building the measurement frameworks the team uses to answer them, and bringing senior leaders along through clear analytical arguments. At the staff level, you’ll be expected to bring deep domain expertise, product intuition and a deep data science tool kit (programming, statistics, analytics) to bear to create initiatives that deliver direct business impact and to drive strategy. Concretely, you might define how a pillar measures long-term member value, lead a working group on evaluating AI-powered product experiences, collaborate with AI colleagues to set the roadmap for AI systems, or build the attribution methodology our team standardizes on for the next two years. You'll partner with PM and Engineering leadership, mentor senior DS, and have strong opinions about how this work should be done.
What we're looking for: A track record of org-level analytical impact (frameworks, methodologies, or measurement systems that outlast the project they were built for) Deep technical expertise: you may have expertise in experimentation and causal inference, or in ads attribution, or in modeling. You write efficient python and SQLcode and can mentor others Deep domain expertise that is relevant for LinkedIn’s business, e.g. from social media, or online marketplaces, or b2b advertising or sale. The ability to make a quantitative argument that changes a director-level decision A point of view on how DS should evolve as products become AI-native Demonstrated growth of senior DS through mentorship and technical leadership
Responsibilities Designs and performs impactful data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop deeper ecosystem understanding, inform measurement framework scalability, and monitor current products or systems. Evaluates A/B and causal tooling for less obvious, yet impactful gaps, and potentially partners with Applied Sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models. Leverage AI tools in day-to-day workflows to increase productivity Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making. Analyzes and interprets internal, complex, unstructured and/or structured datasets, potentially connected with external sources or cross-line-of-business information, to assess business health and understand what drives business outcomes. Explores internal, complex artificial intelligence (AI) models and systems to create understanding and visibility into how they work, how they are tracked, and how they can be improved. Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models. Drives data science best practices and principles through all phases of development of customized and/or scalable data solutions. Advises relevant partners and internal customers on scoping data-driven investigations and experiments, providing strategic recommendations for what best to prioritize to achieve intended strategy. Drives alignment and proactively communicates with Data Science teams and business functions. Identifies patterns of user behavior leveraging advanced data mining techniques and makes data-informed recommendations to internal partners. Creates compelling, data-centric stories and recommendations based on analytical findings to influence and shape internal customers' strategic direction and decision-making.
Qualifications Basic Qualifications Bachelor or higher degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc. 5+ years of relevant work experience. Experience influencing strategy through data-centric presentations . Experience in SQL. Background in at least one programming language (e.g., R, Python, Scala). Experience in applied statistics and statistical modeling in at least one statistical software package. Experience telling stories with data and visualization tools Experience running platform experiments and techniques like A/B testing Preferred Qualifications Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field. Suggested Skills Stakeholder Management Influencing for Impact Strategic Thinking LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $149,000 - $245,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.
We need to strip out the "LinkedIn is committed to fair and equitable compensation practices." maybe keep as part of Benefits? It's about compensation. Could be part of Benefits section.
We need to output only HTML fragment.
Let's craft:
About the role
This role is based in Mountain View, CA and is hybrid, meaning work will be performed both from home and from a LinkedIn office on select days as determined by business needs. You will join LinkedIn's Data Science team, which turns member and customer behavior into product decisions across one of the world's largest professional networks. As a Staff Data Scientist, you will set technical direction across a product pillar, deciding which questions are worth answering, building measurement frameworks, and guiding senior leaders with clear analytical arguments. You are expected to bring deep domain expertise, product intuition, and a strong data science toolkit (programming, statistics, analytics) to drive initiatives with direct business impact and shape strategy. Example activities include defining long‑term member value metrics for a pillar, leading a working group on evaluating AI‑powered product experiences, collaborating with AI colleagues to set the AI systems roadmap, or building the attribution methodology the team will standardize on for the next two years. You will partner with PM and Engineering leadership, mentor senior data scientists, and advocate for best practices in how this work should be done.
Responsibilities
- Designs and performs impactful data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop deeper ecosystem understanding, inform measurement framework scalability, and monitor current products or systems.
- Evaluates A/B and causal tooling for less obvious, yet impactful gaps, and potentially partners with Applied Sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models.
- Leverages AI tools in day-to-day workflows to increase productivity.
- Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making.
- Analyzes and interprets internal, complex, unstructured and/or structured datasets, potentially connected with external sources or cross-line-of-business information, to assess business health and understand what drives business outcomes.
- Explores internal, complex artificial intelligence (AI) models and systems to create understanding and visibility into how they work, how they are tracked, and how they can be improved.
- Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models.
- Drives data science best practices and principles through all phases of development of customized and/or scalable data solutions.
- Advises relevant partners and internal customers on scoping data-driven investigations and experiments, providing strategic recommendations for what best to prioritize to achieve intended strategy.
- Drives alignment and proactively communicates with Data Science teams and business functions.
- Identifies patterns of user behavior leveraging advanced data mining techniques and makes data-informed recommendations to internal partners.
- Creates compelling, data-centric stories and recommendations based on analytical findings to influence and shape internal customers' strategic direction and decision-making.
Qualifications
Basic Qualifications
- Bachelor or higher degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
- 5+ years of relevant work experience.
- Experience influencing strategy through data-centric presentations.
- Experience in SQL.
- Background in at least one programming language (e.g., R, Python, Scala).
- Experience in applied statistics and statistical modeling in at least one statistical software package.
- Experience telling stories with data and visualization tools.
- Experience running platform experiments and techniques like A/B testing.
Preferred Qualifications
- Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
Skills
- Stakeholder Management
- Influencing for Impact
- Strategic Thinking
Pay
The pay range for this role is $149,000 - $245,000
for prose. We need to decide what goes into each section. About the role: could include location, hybrid, team description, role purpose. Responsibilities: list each bullet from "Responsibilities" section. Qualifications: could combine Basic and Preferred? Might split into Basic Qualifications and Preferred Qualifications as subheadings? But only allowed headings are those listed; we could put both under Qualifications heading, with paragraphs separating basic and preferred. Or we could have two sections: Qualifications and Preferred Qualifications? Not in list. Better to keep single Qualifications section with paragraphs. Skills: from "Suggested Skills": Stakeholder Management, Influencing for Impact, Strategic Thinking. Could be list. Benefits: mention total compensation package may include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans; link to benefits. Pay: pay range $149,000 - $245,000. We must not include filler like "commensurate with experience". Not present. We must not include duplicated lines. Let's extract substantive content. From posting: Job Description This role is based in Mountain View, CA. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. LinkedIn's Data Science team turns member and customer behavior into product decisions across one of the world's largest professional networks. We create a deep understanding of our members through data, we design experiments, build metric frameworks, and partner with PM, Engineering, and Design to ship features that create real economic opportunity for members — including the next generation of AI-powered experiences. We're hiring a Staff Data Scientist to set technical direction across a product pillar. You'll be the person deciding which questions are worth answering, building the measurement frameworks the team uses to answer them, and bringing senior leaders along through clear analytical arguments. At the staff level, you’ll be expected to bring deep domain expertise, product intuition and a deep data science tool kit (programming, statistics, analytics) to bear to create initiatives that deliver direct business impact and to drive strategy. Concretely, you might define how a pillar measures long-term member value, lead a working group on evaluating AI-powered product experiences, collaborate with AI colleagues to set the roadmap for AI systems, or build the attribution methodology our team standardizes on for the next two years. You'll partner with PM and Engineering leadership, mentor senior DS, and have strong opinions about how this work should be done. What we're looking for: A track record of org-level analytical impact (frameworks, methodologies, or measurement systems that outlast the project they were built for) Deep technical expertise: you may have expertise in experimentation and causal inference, or in ads attribution, or in modeling. You write efficient python and SQLcode and can mentor others Deep domain expertise that is relevant for LinkedIn’s business, e.g. from social media, or online marketplaces, or b2b advertising or sale. The ability to make a quantitative argument that changes a director-level decision A point of view on how DS should evolve as products become AI-native Demonstrated growth of senior DS through mentorship and technical leadership Responsibilities Designs and performs impactful data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop deeper ecosystem understanding, inform measurement framework scalability, and monitor current products or systems. Evaluates A/B and causal tooling for less obvious, yet impactful gaps, and potentially partners with Applied Sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models. Leverage AI tools in day-to-day workflows to increase productivity Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making. Analyzes and interprets internal, complex, unstructured and/or structured datasets, potentially connected with external sources or cross-line-of-business information, to assess business health and understand what drives business outcomes. Explores internal, complex artificial intelligence (AI) models and systems to create understanding and visibility into how they work, how they are tracked, and how they can be improved. Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models. Drives data science best practices and principles through all phases of development of customized and/or scalable data solutions. Advises relevant partners and internal customers on scoping data-driven investigations and experiments, providing strategic recommendations for what best to prioritize to achieve intended strategy. Drives alignment and proactively communicates with Data Science teams and business functions. Identifies patterns of user behavior leveraging advanced data mining techniques and makes data-informed recommendations to internal partners. Creates compelling, data-centric stories and recommendations based on analytical findings to influence and shape internal customers' strategic direction and decision-making. Qualifications Basic Qualifications Bachelor or higher degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc. 5+ years of relevant work experience. Experience influencing strategy through data-centric presentations . Experience in SQL. Background in at least one programming language (e.g., R, Python, Scala). Experience in applied statistics and statistical modeling in at least one statistical software package. Experience telling stories with data and visualization tools Experience running platform experiments and techniques like A/B testing Preferred Qualifications Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field. Suggested Skills Stakeholder Management Influencing for Impact Strategic Thinking LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $149,000 - $245,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits. We need to strip out the "LinkedIn is committed to fair and equitable compensation practices." maybe keep as part of Benefits? It's about compensation. Could be part of Benefits section. We need to output only HTML fragment. Let's craft:
About the role
This role is based in Mountain View, CA and is hybrid, meaning work will be performed both from home and from a LinkedIn office on select days as determined by business needs. You will join LinkedIn's Data Science team, which turns member and customer behavior into product decisions across one of the world's largest professional networks. As a Staff Data Scientist, you will set technical direction across a product pillar, deciding which questions are worth answering, building measurement frameworks, and guiding senior leaders with clear analytical arguments. You are expected to bring deep domain expertise, product intuition, and a strong data science toolkit (programming, statistics, analytics) to drive initiatives with direct business impact and shape strategy. Example activities include defining long‑term member value metrics for a pillar, leading a working group on evaluating AI‑powered product experiences, collaborating with AI colleagues to set the AI systems roadmap, or building the attribution methodology the team will standardize on for the next two years. You will partner with PM and Engineering leadership, mentor senior data scientists, and advocate for best practices in how this work should be done.
Responsibilities
- Designs and performs impactful data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop deeper ecosystem understanding, inform measurement framework scalability, and monitor current products or systems.
- Evaluates A/B and causal tooling for less obvious, yet impactful gaps, and potentially partners with Applied Sciences teams to create A/B and causal test protocols and methods and analyze ramp performance to help optimize new and/or existing features or models.
- Leverages AI tools in day-to-day workflows to increase productivity.
- Conceptualizes, defines, and socializes foundational metrics to set organizational goals and scale with AI to democratize data-driven decision-making.
- Analyzes and interprets internal, complex, unstructured and/or structured datasets, potentially connected with external sources or cross-line-of-business information, to assess business health and understand what drives business outcomes.
- Explores internal, complex artificial intelligence (AI) models and systems to create understanding and visibility into how they work, how they are tracked, and how they can be improved.
- Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models.
- Drives data science best practices and principles through all phases of development of customized and/or scalable data solutions.
- Advises relevant partners and internal customers on scoping data-driven investigations and experiments, providing strategic recommendations for what best to prioritize to achieve intended strategy.
- Drives alignment and proactively communicates with Data Science teams and business functions.
- Identifies patterns of user behavior leveraging advanced data mining techniques and makes data-informed recommendations to internal partners.
- Creates compelling, data-centric stories and recommendations based on analytical findings to influence and shape internal customers' strategic direction and decision-making.
Qualifications
Basic Qualifications
- Bachelor or higher degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
- 5+ years of relevant work experience.
- Experience influencing strategy through data-centric presentations.
- Experience in SQL.
- Background in at least one programming language (e.g., R, Python, Scala).
- Experience in applied statistics and statistical modeling in at least one statistical software package.
- Experience telling stories with data and visualization tools.
- Experience running platform experiments and techniques like A/B testing.
Preferred Qualifications
- Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
Skills
- Stakeholder Management
- Influencing for Impact
- Strategic Thinking
Pay
The pay range for this role is $149,000 - $245,000