Senior Data Scientist
LinkedIn · Sunnyvale, CA · 1 mo ago
On-siteEngineering$125k–$205k/yrFull-time
We need to output HTML fragment with sections: About the role? The posting includes description paragraphs. We need to use for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections when posting has real specific content.
We need to parse the posting.
Content:
- Intro about LinkedIn (company description) maybe not needed? The job description starts after "Job Description". The description includes: "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 design experiments, investigate metric movements, and partner with PM and Engineering to ship features that create real economic opportunity for members, including the next generation of AI-powered experiences. We're hiring a Senior data scientist to own ambiguous, high-impact problems end-to-end. You’ll use your strong statistical intuition and well-honed data science tool kit to answer open-ended problems like defining long-term retention or marketplace value. You’ll define the metrics that guide product strategy, dive deep into marketplaces and evaluate AI-native features as they ship. You'll work closely with engineers and PMs to translate findings into roadmap decisions."
That could be "About the role" section.
Then "Responsibilities" list: bullet points after "Responsibilities". Let's extract them:
- Designs and performs data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems.
- Evaluates A/B and causal tooling for discernable 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
- Partners with internal customers to translate business problems into data-verifiable hypotheses.
- Develops and employs models and causal analyses (e.g., panel analysis, A/B testing) to determine what drives metric relationships and create actionable insights within moderately complex product domains.
- Independently diagnoses issues and identifies opportunities to drive improvements within AI/LLM systems.
- Prototypes data solutions, and/or collaborates with relevant partners (e.g., Data Science teams, Engineering) to operationalize, build, and validate scalable tools/applications/solutions for use by internal customers to convert data into insights.
- Embeds data science best practices and principles through all phases of development of customized and/or scalable data solutions, providing feedback to junior team members.
- Communicates with relevant partners and internal customers on scoping data-driven investigations and experiments, contributing to recommendations for what best to prioritize to achieve intended strategy.
- Develops and runs predictive models for estimating the impact of initiatives on identified KPIs.
- Independently creates compelling, data-centric stories based on analytical findings to contribute to internal customers' decision-making.
That's a list.
Then "Qualifications" section: includes Basic Qualifications and Preferred Qualifications. We need to decide whether to split into Requirements and Qualifications? The instruction says use headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content.
"Requirements" likely corresponds to Basic Qualifications. "Qualifications" maybe Preferred Qualifications? Or we could combine. But we need to follow headings exactly. We'll include both Requirements and Qualifications sections if content exists.
Basic Qualifications bullet list:
- Bachelor's Degree in a quantitative discipline: Computer Science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
- 3+ years of relevant industry or relevant academia experience working with large amounts of data
- 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 bullet list:
- Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
- Experience with SQL & Python, experimentation (design, analysis, variance reduction),
- Experience with causal inference methods; Product intuition for two-sided marketplaces
- Clear written communication
- Ability to understand how to measure AI-powered product experiences.
We could put Basic under Requirements, Preferred under Qualifications.
Then "Skills" section: "Suggested Skills Communication Decision Making Data Science Domain Knowledge and Skills Strategic Thinking". That's a list of skills: Communication, Decision Making, Data Science Domain Knowledge and Skills, Strategic Thinking. Could be a list.
Benefits: The posting includes: "We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels." Also later: "LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $125,000 to $205,000. 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."
Thus we have Benefits (generous health and wellness programs and time away) and Pay (range). Also maybe mention bonus, stock etc. We'll include Benefits section with that description. Pay section with the range and note about bonus/stock etc. Schedule: The posting mentions hybrid work location: "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." That's schedule info. So we can include Schedule section with that.
We must not include boilerplate like EEO, legal, etc. Remove all that.
We must not repeat job title as heading. Not include Job ID.
We must output only HTML fragment, no /, no markdown fences.
We'll produce something like:
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections when posting has real specific content.
We need to parse the posting.
Content:
- Intro about LinkedIn (company description) maybe not needed? The job description starts after "Job Description". The description includes: "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 design experiments, investigate metric movements, and partner with PM and Engineering to ship features that create real economic opportunity for members, including the next generation of AI-powered experiences. We're hiring a Senior data scientist to own ambiguous, high-impact problems end-to-end. You’ll use your strong statistical intuition and well-honed data science tool kit to answer open-ended problems like defining long-term retention or marketplace value. You’ll define the metrics that guide product strategy, dive deep into marketplaces and evaluate AI-native features as they ship. You'll work closely with engineers and PMs to translate findings into roadmap decisions."
That could be "About the role" section.
Then "Responsibilities" list: bullet points after "Responsibilities". Let's extract them:
- Designs and performs data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems.
- Evaluates A/B and causal tooling for discernable 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
- Partners with internal customers to translate business problems into data-verifiable hypotheses.
- Develops and employs models and causal analyses (e.g., panel analysis, A/B testing) to determine what drives metric relationships and create actionable insights within moderately complex product domains.
- Independently diagnoses issues and identifies opportunities to drive improvements within AI/LLM systems.
- Prototypes data solutions, and/or collaborates with relevant partners (e.g., Data Science teams, Engineering) to operationalize, build, and validate scalable tools/applications/solutions for use by internal customers to convert data into insights.
- Embeds data science best practices and principles through all phases of development of customized and/or scalable data solutions, providing feedback to junior team members.
- Communicates with relevant partners and internal customers on scoping data-driven investigations and experiments, contributing to recommendations for what best to prioritize to achieve intended strategy.
- Develops and runs predictive models for estimating the impact of initiatives on identified KPIs.
- Independently creates compelling, data-centric stories based on analytical findings to contribute to internal customers' decision-making.
That's a list.
Then "Qualifications" section: includes Basic Qualifications and Preferred Qualifications. We need to decide whether to split into Requirements and Qualifications? The instruction says use headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content.
"Requirements" likely corresponds to Basic Qualifications. "Qualifications" maybe Preferred Qualifications? Or we could combine. But we need to follow headings exactly. We'll include both Requirements and Qualifications sections if content exists.
Basic Qualifications bullet list:
- Bachelor's Degree in a quantitative discipline: Computer Science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
- 3+ years of relevant industry or relevant academia experience working with large amounts of data
- 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 bullet list:
- Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
- Experience with SQL & Python, experimentation (design, analysis, variance reduction),
- Experience with causal inference methods; Product intuition for two-sided marketplaces
- Clear written communication
- Ability to understand how to measure AI-powered product experiences.
We could put Basic under Requirements, Preferred under Qualifications.
Then "Skills" section: "Suggested Skills Communication Decision Making Data Science Domain Knowledge and Skills Strategic Thinking". That's a list of skills: Communication, Decision Making, Data Science Domain Knowledge and Skills, Strategic Thinking. Could be a list.
Benefits: The posting includes: "We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels." Also later: "LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $125,000 to $205,000. 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."
Thus we have Benefits (generous health and wellness programs and time away) and Pay (range). Also maybe mention bonus, stock etc. We'll include Benefits section with that description. Pay section with the range and note about bonus/stock etc. Schedule: The posting mentions hybrid work location: "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." That's schedule info. So we can include Schedule section with that.
We must not include boilerplate like EEO, legal, etc. Remove all that.
We must not repeat job title as heading. Not include Job ID.
We must output only HTML fragment, no /, no markdown fences.
We'll produce something like:
About the role
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 design experiments, investigate metric movements, and partner with PM and Engineering to ship features that create real economic opportunity for members, including the next generation of AI-powered experiences. We're hiring a Senior data scientist to own ambiguous, high-impact problems end-to-end. You’ll use your strong statistical intuition and well-honed data science tool kit to answer open-ended problems like defining long-term retention or marketplace value. You’ll define the metrics that guide product strategy, dive deep into marketplaces and evaluate AI-native features as they ship. You'll work closely with engineers and PMs to translate findings into roadmap decisions.
Responsibilities
- Designs and performs data-driven experiments and causal analyses to test and validate new product ideas or go-to-market strategies, develop ecosystem understanding, and monitor current products or systems.
- Evaluates A/B and causal tooling for discernable 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
- Partners with internal customers to translate business problems into data-verifiable hypotheses.
- Develops and employs models and causal analyses (e.g., panel analysis, A/B testing) to determine what drives metric relationships and create actionable insights within moderately complex product domains.
- Independently diagnoses issues and identifies opportunities to drive improvements within AI/LLM systems.
- Prototypes data solutions, and/or collaborates with relevant partners (e.g., Data Science teams, Engineering) to operationalize, build, and validate scalable tools/applications/solutions for use by internal customers to convert data into insights.
- Embeds data science best practices and principles through all phases of development of customized and/or scalable data solutions, providing feedback to junior team members.
- Communicates with relevant partners and internal customers on scoping data-driven investigations and experiments, contributing to recommendations for what best to prioritize to achieve intended strategy.
- Develops and runs predictive models for estimating the impact of initiatives on identified KPIs.
- Independently creates compelling, data-centric stories based on analytical findings to contribute to internal customers' decision-making.
Requirements
- Bachelor's Degree in a quantitative discipline: Computer Science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc.
- 3+ years of relevant industry or relevant academia experience working with large amounts of data
- 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.
Qualifications
- Master's Degree or Doctorate in Applied Mathematics, Computer Science, Data Science, Economics, Engineering, Informatics, Statistics, or related field.
- Experience with SQL & Python, experimentation (design, analysis, variance reduction)
- Experience with causal inference methods; Product intuition for two-sided marketplaces
- Clear written communication
- Ability to understand how to measure AI-powered product experiences.
Skills
- Communication
- Decision Making
- Data Science Domain Knowledge and Skills
- Strategic Thinking
Benefits
We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.
Pay
The pay range for this role is $125,000 to $205,000. 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.
Schedule
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.
Check for any repeated content: The hybrid description appears also in About the role paragraph; we included it there as well. Might be duplicate but okay. Could remove from About the role to avoid duplication, but not required. The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. Duplicate is okay as it's still preserving. However we might want to avoid redundancy but not required. It's fine. Check for any boilerplate: