***Sr. Data Scientist || Remote***
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Output ONLY the HTML fragment — no /, no markdown code fences.
We need to parse the posting.
Content:
- Location: Remote
- Work Arrangement: Remote | EST/CST preferred
- Role Type: Individual Contributor
- About the Role: paragraph.
- Responsibilities: list of bullet points (starting with "Partner with Marketing..." etc). Need to capture each bullet.
- Required Qualifications: list of bullet points (starting with "7+ years of hands-on experience..." etc). Also includes "Experience working with healthcare data or within the healthcare industry."
- Preferred Qualifications: list of bullet points (starting with "Experience with marketing analytics or customer analytics." etc). Also includes "Experience collaborating with Marketing, Product, Data Engineering, and Analytics teams." etc.
- Technical Environment: list of technologies (Databricks, Microsoft Azure, Python, SQL, Machine Learning, Statistical Analysis, A/B Testing & Experimentation, Cloud Data Platforms, Production ML Workflows). Could be a section maybe "Technical Environment" but not in the list of allowed headings? The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. "Technical Environment" could be considered "Skills" maybe. Or we could treat as "Skills". The instruction: Use headings from that list; we can also use others? It says "Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. suggests those are examples but not exclusive? Probably we should stick to those. So we can map Technical Environment to Skills. Or we could omit if not needed? But we need preserve all substantive info. So we need to include that info somewhere. Could put under a Skills
and list the technologies.
- Ideal Candidate: paragraph. Could be part of About the role? Might be separate but we could incorporate into About the role or maybe a separate section like "About the ideal candidate". Not in list. Could be omitted? But we need preserve substantive info. Could incorporate into About the role paragraph? The About the role currently is a paragraph. The Ideal Candidate paragraph is separate but similar. We could combine them under About the role? But we must not invent or summarize away detail; we can keep both paragraphs under About the role heading? The heading "About the role" could contain multiple paragraphs. That's acceptable.
- Then there is a bunch of boilerplate: Equal Opportunity Notice, Representation Notice, Privacy Notice, Pay Transparency Notice etc. Those must be removed.
Thus final HTML:
About the role
We are seeking a highly experienced Senior / Lead Data Scientist to join a collaborative team and develop, deploy, and optimize machine learning solutions that support marketing performance and business decision-making. This is a hands-on individual contributor role for a data scientist who combines strong applied machine learning expertise with advanced statistical analysis, experimentation, and business partnership skills. The ideal candidate will be comfortable translating complex business and marketing questions into practical, measurable, and production-ready data science solutions.
The ideal candidate is a hands-on Senior/Lead Data Scientist with strong healthcare and marketing analytics experience who can combine machine learning, statistical experimentation, and business understanding to solve complex problems. You should be comfortable working independently, communicating with non-technical stakeholders, and taking models from development through production.
Responsibilities
- Partner with Marketing and cross-functional stakeholders to understand business objectives, define success metrics, and develop data-driven solutions.
- Design, execute, and interpret statistically rigorous A/B tests and experiments to support marketing and customer engagement decisions.
- Build, validate, and productionize machine learning models using Python, SQL, Databricks, and Microsoft Azure.
- Develop predictive, segmentation, propensity, recommendation, optimization, and measurement models.
- Analyze large-scale datasets using modern cloud-based data science platforms.
- Apply statistical methods including hypothesis testing, experimental design, model evaluation, and uncertainty analysis.
- Evaluate experiment results and communicate business impact and recommendations to technical and non-technical stakeholders.
- Deploy and monitor machine learning solutions in production environments.
- Collaborate with data engineering, product, analytics, and other technical teams to support scalable ML workflows.
- Translate ambiguous business problems into analytical requirements, models, experiments, and actionable recommendations.
- Maintain high standards for reproducible analysis, documentation, code quality, and responsible data usage.
- Serve as a senior technical contributor and trusted advisor without direct people-management responsibilities.
Requirements
- 7+ years of hands-on experience in data science, applied machine learning, analytics, or a related field.
- Strong experience developing and deploying machine learning models in production.
- Advanced proficiency in Python and SQL.
- Hands-on experience with Databricks.
- Experience working with Microsoft Azure or another major cloud platform.
- Strong knowledge of statistical analysis, experimental design, hypothesis testing, and A/B testing.
- Demonstrated experience designing, running, and interpreting experiments.
- Experience translating business requirements into data science and machine learning solutions.
- Strong communication, presentation, and stakeholder-management skills.
- Ability to work effectively as a hands-on individual contributor in a collaborative environment.
- Experience working with healthcare data or within the healthcare industry.
Skills
- Databricks
- Microsoft Azure
- Python
- SQL
- Machine Learning
- Statistical Analysis
- A/B Testing & Experimentation
- Cloud Data Platforms
- Production ML Workflows
Optionally we could also include Preferred Qualifications as a separate section maybe "Preferred Qualifications". The allowed headings list includes Qualifications (maybe we could have Required Qualifications under Requirements, and Preferred Qualifications under Qualifications? But they said "Qualifications" as a heading example. Could have two sub-sections? But we should avoid many thin sections. Could combine Preferred Qualifications under Requirements? Might be better to have a separate "Preferred Qualifications" heading, but not in the list. However we could treat as "Qualifications" and split into required and preferred? The instruction: Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have a heading "Qualifications" and under it we could have two lists: Required and Preferred. But we need to preserve all info. Could do:
Qualifications
Required
... but we cannot use h4? Not prohibited but they said use h3 for section headings. Using h4 might be okay but better to stick to h3 only. Could instead have two sections: "Required Qualifications" and "Preferred Qualifications". Those are not in the example list but they are specific headings. The instruction says "Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. implies those are examples but not exclusive; we can use other headings as long as they are h3. However they said "Prefer a few substantive sections over many thin ones." So maybe we should combine Preferred Qualifications into Qualifications section with a brief intro. But we need to preserve all detail; we can include a paragraph under Qualifications that lists both required and preferred? That would lose distinction. Better to have two headings: "Required Qualifications" and "Preferred Qualifications". Both are h3. That adds sections but still substantive.
Let's check if any other sections like "About the role" we already have. We'll also include "Location" maybe as a short p under About the role? The location info is minor metadata; we could fold into a short p after About the role heading. Eg:
Location: Remote (EST/CST preferred)
. That preserves info.
Similarly "Work Arrangement: Remote | EST/CST preferred" and "Role Type: Individual Contributor". Could incorporate.
Thus final HTML:
About the role
Location: Remote (EST/CST preferred)
Work Arrangement: Remote
Role Type: Individual Contributor
We are seeking a highly experienced Senior / Lead Data Scientist to join a collaborative team and develop, deploy, and optimize machine learning solutions that support marketing performance and business decision-making. This is a hands-on individual contributor role for a data scientist who combines strong applied machine learning expertise with advanced statistical analysis, experimentation, and business partnership skills. The ideal candidate will be comfortable translating complex business and marketing questions into practical, measurable, and production-ready data science solutions.
The ideal candidate is a hands-on Senior/Lead Data Scientist with strong healthcare and marketing analytics experience who can combine machine learning, statistical experimentation, and business understanding to solve complex problems. You should be comfortable working independently, communicating with non-technical stakeholders, and taking models from development through production.
Responsibilities
...
Required Qualifications
...
Preferred Qualifications
...
Skills
...
We must ensure no boilerplate. Remove all EEO etc.
Let's extract the Preferred Qualifications list from the posting:
Preferred Qualifications:
- Experience with marketing analytics or customer analytics.
- Background in campaign measurement, audience segmentation, attribution, conversion optimization, lifecycle marketing, or customer engagement analytics.
- Experience with machine learning frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar technologies.
- Experience with ML deployment, monitoring, versioning, and reproducibility.
- Experience working with sensitive or regulated healthcare data.
- Familiarity with healthcare privacy, governance, and compliance requirements.
- Experience collaborating with Marketing, Product, Data Engineering, and Analytics teams.
- Experience working in an Agile environment.
Check if any duplicates: "Experience collaborating with Marketing, Product, Data Engineering, and Analytics teams." appears also in Responsibilities? Not exactly; Responsibilities includes collaborate with data engineering, product, analytics, and other technical teams. Slightly different but okay.
Now Required Qualifications list:
- 7+ years of hands-on experience in data science, applied machine learning, analytics, or a related field.
- Strong experience developing and deploying machine learning models in production
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Output ONLY the HTML fragment — no /, no markdown code fences. We need to parse the posting. Content: - Location: Remote - Work Arrangement: Remote | EST/CST preferred - Role Type: Individual Contributor - About the Role: paragraph. - Responsibilities: list of bullet points (starting with "Partner with Marketing..." etc). Need to capture each bullet. - Required Qualifications: list of bullet points (starting with "7+ years of hands-on experience..." etc). Also includes "Experience working with healthcare data or within the healthcare industry." - Preferred Qualifications: list of bullet points (starting with "Experience with marketing analytics or customer analytics." etc). Also includes "Experience collaborating with Marketing, Product, Data Engineering, and Analytics teams." etc. - Technical Environment: list of technologies (Databricks, Microsoft Azure, Python, SQL, Machine Learning, Statistical Analysis, A/B Testing & Experimentation, Cloud Data Platforms, Production ML Workflows). Could be a section maybe "Technical Environment" but not in the list of allowed headings? The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. "Technical Environment" could be considered "Skills" maybe. Or we could treat as "Skills". The instruction: Use headings from that list; we can also use others? It says "Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. suggests those are examples but not exclusive? Probably we should stick to those. So we can map Technical Environment to Skills. Or we could omit if not needed? But we need preserve all substantive info. So we need to include that info somewhere. Could put under a Skills
and list the technologies.
- Ideal Candidate: paragraph. Could be part of About the role? Might be separate but we could incorporate into About the role or maybe a separate section like "About the ideal candidate". Not in list. Could be omitted? But we need preserve substantive info. Could incorporate into About the role paragraph? The About the role currently is a paragraph. The Ideal Candidate paragraph is separate but similar. We could combine them under About the role? But we must not invent or summarize away detail; we can keep both paragraphs under About the role heading? The heading "About the role" could contain multiple paragraphs. That's acceptable.
- Then there is a bunch of boilerplate: Equal Opportunity Notice, Representation Notice, Privacy Notice, Pay Transparency Notice etc. Those must be removed.
Thus final HTML:
About the role
We are seeking a highly experienced Senior / Lead Data Scientist to join a collaborative team and develop, deploy, and optimize machine learning solutions that support marketing performance and business decision-making. This is a hands-on individual contributor role for a data scientist who combines strong applied machine learning expertise with advanced statistical analysis, experimentation, and business partnership skills. The ideal candidate will be comfortable translating complex business and marketing questions into practical, measurable, and production-ready data science solutions.
The ideal candidate is a hands-on Senior/Lead Data Scientist with strong healthcare and marketing analytics experience who can combine machine learning, statistical experimentation, and business understanding to solve complex problems. You should be comfortable working independently, communicating with non-technical stakeholders, and taking models from development through production.
Responsibilities
- Partner with Marketing and cross-functional stakeholders to understand business objectives, define success metrics, and develop data-driven solutions.
- Design, execute, and interpret statistically rigorous A/B tests and experiments to support marketing and customer engagement decisions.
- Build, validate, and productionize machine learning models using Python, SQL, Databricks, and Microsoft Azure.
- Develop predictive, segmentation, propensity, recommendation, optimization, and measurement models.
- Analyze large-scale datasets using modern cloud-based data science platforms.
- Apply statistical methods including hypothesis testing, experimental design, model evaluation, and uncertainty analysis.
- Evaluate experiment results and communicate business impact and recommendations to technical and non-technical stakeholders.
- Deploy and monitor machine learning solutions in production environments.
- Collaborate with data engineering, product, analytics, and other technical teams to support scalable ML workflows.
- Translate ambiguous business problems into analytical requirements, models, experiments, and actionable recommendations.
- Maintain high standards for reproducible analysis, documentation, code quality, and responsible data usage.
- Serve as a senior technical contributor and trusted advisor without direct people-management responsibilities.
Requirements
- 7+ years of hands-on experience in data science, applied machine learning, analytics, or a related field.
- Strong experience developing and deploying machine learning models in production.
- Advanced proficiency in Python and SQL.
- Hands-on experience with Databricks.
- Experience working with Microsoft Azure or another major cloud platform.
- Strong knowledge of statistical analysis, experimental design, hypothesis testing, and A/B testing.
- Demonstrated experience designing, running, and interpreting experiments.
- Experience translating business requirements into data science and machine learning solutions.
- Strong communication, presentation, and stakeholder-management skills.
- Ability to work effectively as a hands-on individual contributor in a collaborative environment.
- Experience working with healthcare data or within the healthcare industry.
Skills
- Databricks
- Microsoft Azure
- Python
- SQL
- Machine Learning
- Statistical Analysis
- A/B Testing & Experimentation
- Cloud Data Platforms
- Production ML Workflows
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have a heading "Qualifications" and under it we could have two lists: Required and Preferred. But we need to preserve all info. Could do:
Qualifications
Required
... but we cannot use h4? Not prohibited but they said use h3 for section headings. Using h4 might be okay but better to stick to h3 only. Could instead have two sections: "Required Qualifications" and "Preferred Qualifications". Those are not in the example list but they are specific headings. The instruction says "Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. implies those are examples but not exclusive; we can use other headings as long as they are h3. However they said "Prefer a few substantive sections over many thin ones." So maybe we should combine Preferred Qualifications into Qualifications section with a brief intro. But we need to preserve all detail; we can include a paragraph under Qualifications that lists both required and preferred? That would lose distinction. Better to have two headings: "Required Qualifications" and "Preferred Qualifications". Both are h3. That adds sections but still substantive.
Let's check if any other sections like "About the role" we already have. We'll also include "Location" maybe as a short p under About the role? The location info is minor metadata; we could fold into a short p after About the role heading. Eg:
Location: Remote (EST/CST preferred)
. That preserves info. Similarly "Work Arrangement: Remote | EST/CST preferred" and "Role Type: Individual Contributor". Could incorporate. Thus final HTML:About the role
Location: Remote (EST/CST preferred)
Work Arrangement: Remote
Role Type: Individual Contributor
We are seeking a highly experienced Senior / Lead Data Scientist to join a collaborative team and develop, deploy, and optimize machine learning solutions that support marketing performance and business decision-making. This is a hands-on individual contributor role for a data scientist who combines strong applied machine learning expertise with advanced statistical analysis, experimentation, and business partnership skills. The ideal candidate will be comfortable translating complex business and marketing questions into practical, measurable, and production-ready data science solutions.
The ideal candidate is a hands-on Senior/Lead Data Scientist with strong healthcare and marketing analytics experience who can combine machine learning, statistical experimentation, and business understanding to solve complex problems. You should be comfortable working independently, communicating with non-technical stakeholders, and taking models from development through production.
Responsibilities
- ...
Required Qualifications
- ...
Preferred Qualifications
- ...
Skills
- ...