Senior Data Scientist - Generative AI (Data Science Lab)
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. Do NOT put Job ID etc. Remove boilerplate. Preserve all substantive info.
We need to parse content.
The posting: "Senior Data Scientist (Generative AI), Data Science Lab Direct Hire Hybrid Office locations: NYC, NY | Holmdel, NJ | Stamford, CT | Bethlehem, PA, Boston, MA We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads on high-impact high-visibility projects to deliver AI/ML solutions that will be market-tested and deployed to make a real difference to risk management and the Company’s overall financial performance. Successful candidates bring expertise in insurance and financial services, a passion for applying cutting-edge ML and AI insights, and the ability to design and implement data science capabilities that foster growth, competitive advantage, and customer satisfaction."
Then a list of responsibilities (some repeated). Then "You Are Passionate about ..." then "You Have ..." etc.
We need to extract responsibilities bullet list.
Responsibilities (from repeated block):
- Develop Deep Learning/Large Language Model/Generative AI capabilities
- Mapping and mining unstructured data such as insurance contracts, medical records, sale notes, and customer servicing logs
- AI/ML solutions include but not limited to enhancing underwriting risk assessment, claims auto adjudication, and customer servicing
- Run large-scale experiments, from unsupervised pre-training, to fine-tuning, retrieval augmentation and prompt engineering
- Scaling LLM models both in development and in production
- Design and develop high-quality prompts and templates that guide the behavior and responses of LLM. Craft prompts to elicit specific information or control the model's output, ensuring desired accuracy, relevance, and language fluency. Optimize prompts to improve user interactions and system performance
- Evaluate LLM models on statistical tests, business metrics, and bias and other regulatory metrics
- Develop Enterprise Test and Learn Capabilities
- Investigating the current state of the art of experimentation practices and causal inferencing/ML techniques identifying opportunities for upscaling the methodology best practices
- Develop and execute advanced data-driven experiments to optimize various aspects of Guardian’s business
- Creation of test hypothesis, experiment design including KPI selection, and collection and analysis of data
- Support and help build the Data Science Lab (DSL)
- Support use case development that includes initial data exploration, project/sample design, reception and processing of data, performing analysis and modeling to creation of final report/presentation
- Data wrangling/data matching/ETL to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets
- Utilizing advanced statistical and AI/ML techniques to create high-performing predictive models and creative analyses to address business objectives and partner needs
- Identification of source data and data quality checks both in model/solution development and in production
- Packaging of model/solution and deployment in cooperation with Data Engineers and MLOps
- Contribute to the overall Data Science organization
- Collaborate with cross-functional teams of other Data Science, Data Engineering, Business groups
- Contribute to standardization of Data Science tools, processes, and best practices
We need to avoid duplication. The block appears twice; we should deduplicate.
Now "You Are Passionate about ..." is more about personal traits; could go under Skills or Qualifications? Might be part of "Skills" (soft skills). The "You Have" block lists qualifications: PhD with 2+ years experience, Master's with 4+ years experience in Statistics, Computer Science, Engineering, Applied mathematics or related field; 3+ years hands-on ML modeling/development experience; Strong theoretical foundations in probability & statistics, and causal inferencing techniques; Extensive experience in deep learning models including LLM and NLP; Hands-on experience with GPU, distributed computing and applying parallelism to ML solutions; Strong programming skills in Python including PyTorch and/or Tensorflow; Solid background in algorithms and a range of ML models; Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level; Excellent analytical and problem-solving abilities with superb attention to detail; Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success.
We can separate into Requirements (education, experience) and Qualifications (skills). But the instruction says use headings like Requirements, Qualifications, Skills. We need to decide what goes where.
Typically: Requirements = mandatory experience/education; Qualifications = preferred? Skills = technical skills. But we can map.
We have: "You Have PhD with 2+ years of experience, Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field" => education/experience requirement.
"3+ years of hands-on ML modeling/development experience" => requirement.
"Strong theoretical foundations in probability & statistics, and causal inferencing techniques" => skill/qualification.
"Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)" => skill.
"Hands-on experience with GPU, distributed computing and applying parallelism to ML solutions" => skill.
"Strong programming skills in Python including PyTorch and/or Tensorflow" => skill.
"Solid background in algorithms and a range of ML models" => skill.
"Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level" => skill (soft).
"Excellent analytical and problem-solving abilities with superb attention to detail" => skill.
"Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success" => skill/leadership.
"You Are Passionate about cutting-edge technology and keen on applying new AI/ML algorithms and approaches. You are analytically driven, intellectually curious, and experienced leading the development and implementation of data and analytic solutions to solve challenging business problems. You enjoy collaborating with other data scientists to crack hard to solve problems with AI/ML and seeing it deployed in-market and generating value for the Company. You enjoy collaborating with a multi-disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders." => these are more about personal traits, could go under Skills (soft skills) or maybe a separate "About the role" but we already have about role.
We can create sections:
About the role
... summary paragraph(s)
Responsibilities
- ... list items
Requirements
- PhD with 2+ years of experience, or Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field
- 3+ years of hands‑on ML modeling/development experience
Qualifications
- Strong theoretical foundations in probability & statistics, and causal inferencing techniques
- Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)
- Hands‑on experience with GPU, distributed computing and applying parallelism to ML solutions
- Strong programming skills in Python including PyTorch and/or Tensorflow
- Solid background in algorithms and a range of ML models
- Excellent communication skills and ability to work cross‑functionally with Product, Engineering, and other disciplines at leadership and hands‑on levels
- Excellent analytical and problem‑solving abilities with superb attention to detail
- Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success
Skills
maybe combine with Qualifications? But we already used Qualifications for many. Could treat Skills as technical skills: maybe list programming, ML, LLM, prompt engineering, etc. However we already listed many technical skills in Qualifications. To avoid duplication, we could merge Qualifications and Skills into one section, but instruction says we can have multiple sections if substantive. We could keep Qualifications as above and Skills for soft/personal traits: "Passionate about cutting-edge technology", "analytically driven", "intellectually curious", "enjoy collaborating", etc.
Thus Skills section:
- Passionate about cutting‑edge technology and keen on applying new AI/ML algorithms and approaches
- Analytically driven and intellectually curious
- Experienced leading the development and implementation of data and analytic solutions to solve challenging business problems
- Enjoy collaborating with other data scientists to crack hard‑to‑solve problems with AI/ML and seeing it deployed in‑market
- Enjoy collaborating with a multi‑disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders
We should not include Benefits, Pay, Schedule as no info.
Now ensure we preserve all substantive info: we included office locations? The posting includes "Office locations: NYC, NY | Holmdel, NJ | Stamford, CT | Bethlehem, PA, Boston, MA". Should we include that? It's location info, maybe part of About the role. We can include in About the role paragraph.
Also "Direct Hire Hybrid". Could mention.
Thus About the role paragraph: "Senior Data Scientist (Generative AI), Data Science Lab Direct Hire Hybrid. Office locations: NYC, NY; Holmdel, NJ; Stamford, CT; Bethlehem, PA; Boston, MA. We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads on high-impact high-visibility projects to deliver AI/ML solutions that will be market-tested and deployed to make a real difference to risk management and the Company’s overall financial performance. Successful candidates bring expertise in insurance and financial services, a passion for applying cutting-edge ML and AI insights, and the ability to design and implement data science capabilities that foster growth, competitive advantage, and customer satisfaction."
We need to ensure no duplication.
Now produce HTML fragment: only the sections.
Let's craft.
Be careful to escape any HTML special characters? Not needed.
We'll output:
About the role
Senior Data Scientist (Generative AI), Data Science Lab Direct Hire Hybrid. Office locations: NYC, NY; Holmdel, NJ; Stamford, CT; Bethlehem, PA; Boston, MA. We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads on high-impact high-visibility projects to deliver AI
for prose. Do NOT repeat the job title as a heading. Do NOT put Job ID etc. Remove boilerplate. Preserve all substantive info. We need to parse content. The posting: "Senior Data Scientist (Generative AI), Data Science Lab Direct Hire Hybrid Office locations: NYC, NY | Holmdel, NJ | Stamford, CT | Bethlehem, PA, Boston, MA We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads on high-impact high-visibility projects to deliver AI/ML solutions that will be market-tested and deployed to make a real difference to risk management and the Company’s overall financial performance. Successful candidates bring expertise in insurance and financial services, a passion for applying cutting-edge ML and AI insights, and the ability to design and implement data science capabilities that foster growth, competitive advantage, and customer satisfaction." Then a list of responsibilities (some repeated). Then "You Are Passionate about ..." then "You Have ..." etc. We need to extract responsibilities bullet list. Responsibilities (from repeated block): - Develop Deep Learning/Large Language Model/Generative AI capabilities - Mapping and mining unstructured data such as insurance contracts, medical records, sale notes, and customer servicing logs - AI/ML solutions include but not limited to enhancing underwriting risk assessment, claims auto adjudication, and customer servicing - Run large-scale experiments, from unsupervised pre-training, to fine-tuning, retrieval augmentation and prompt engineering - Scaling LLM models both in development and in production - Design and develop high-quality prompts and templates that guide the behavior and responses of LLM. Craft prompts to elicit specific information or control the model's output, ensuring desired accuracy, relevance, and language fluency. Optimize prompts to improve user interactions and system performance - Evaluate LLM models on statistical tests, business metrics, and bias and other regulatory metrics - Develop Enterprise Test and Learn Capabilities - Investigating the current state of the art of experimentation practices and causal inferencing/ML techniques identifying opportunities for upscaling the methodology best practices - Develop and execute advanced data-driven experiments to optimize various aspects of Guardian’s business - Creation of test hypothesis, experiment design including KPI selection, and collection and analysis of data - Support and help build the Data Science Lab (DSL) - Support use case development that includes initial data exploration, project/sample design, reception and processing of data, performing analysis and modeling to creation of final report/presentation - Data wrangling/data matching/ETL to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets - Utilizing advanced statistical and AI/ML techniques to create high-performing predictive models and creative analyses to address business objectives and partner needs - Identification of source data and data quality checks both in model/solution development and in production - Packaging of model/solution and deployment in cooperation with Data Engineers and MLOps - Contribute to the overall Data Science organization - Collaborate with cross-functional teams of other Data Science, Data Engineering, Business groups - Contribute to standardization of Data Science tools, processes, and best practices We need to avoid duplication. The block appears twice; we should deduplicate. Now "You Are Passionate about ..." is more about personal traits; could go under Skills or Qualifications? Might be part of "Skills" (soft skills). The "You Have" block lists qualifications: PhD with 2+ years experience, Master's with 4+ years experience in Statistics, Computer Science, Engineering, Applied mathematics or related field; 3+ years hands-on ML modeling/development experience; Strong theoretical foundations in probability & statistics, and causal inferencing techniques; Extensive experience in deep learning models including LLM and NLP; Hands-on experience with GPU, distributed computing and applying parallelism to ML solutions; Strong programming skills in Python including PyTorch and/or Tensorflow; Solid background in algorithms and a range of ML models; Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level; Excellent analytical and problem-solving abilities with superb attention to detail; Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success. We can separate into Requirements (education, experience) and Qualifications (skills). But the instruction says use headings like Requirements, Qualifications, Skills. We need to decide what goes where. Typically: Requirements = mandatory experience/education; Qualifications = preferred? Skills = technical skills. But we can map. We have: "You Have PhD with 2+ years of experience, Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field" => education/experience requirement. "3+ years of hands-on ML modeling/development experience" => requirement. "Strong theoretical foundations in probability & statistics, and causal inferencing techniques" => skill/qualification. "Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)" => skill. "Hands-on experience with GPU, distributed computing and applying parallelism to ML solutions" => skill. "Strong programming skills in Python including PyTorch and/or Tensorflow" => skill. "Solid background in algorithms and a range of ML models" => skill. "Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level" => skill (soft). "Excellent analytical and problem-solving abilities with superb attention to detail" => skill. "Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success" => skill/leadership. "You Are Passionate about cutting-edge technology and keen on applying new AI/ML algorithms and approaches. You are analytically driven, intellectually curious, and experienced leading the development and implementation of data and analytic solutions to solve challenging business problems. You enjoy collaborating with other data scientists to crack hard to solve problems with AI/ML and seeing it deployed in-market and generating value for the Company. You enjoy collaborating with a multi-disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders." => these are more about personal traits, could go under Skills (soft skills) or maybe a separate "About the role" but we already have about role. We can create sections:
About the role
... summary paragraph(s)
Responsibilities
- ... list items
Requirements
- PhD with 2+ years of experience, or Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field
- 3+ years of hands‑on ML modeling/development experience
Qualifications
- Strong theoretical foundations in probability & statistics, and causal inferencing techniques
- Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)
- Hands‑on experience with GPU, distributed computing and applying parallelism to ML solutions
- Strong programming skills in Python including PyTorch and/or Tensorflow
- Solid background in algorithms and a range of ML models
- Excellent communication skills and ability to work cross‑functionally with Product, Engineering, and other disciplines at leadership and hands‑on levels
- Excellent analytical and problem‑solving abilities with superb attention to detail
- Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success
Skills
maybe combine with Qualifications? But we already used Qualifications for many. Could treat Skills as technical skills: maybe list programming, ML, LLM, prompt engineering, etc. However we already listed many technical skills in Qualifications. To avoid duplication, we could merge Qualifications and Skills into one section, but instruction says we can have multiple sections if substantive. We could keep Qualifications as above and Skills for soft/personal traits: "Passionate about cutting-edge technology", "analytically driven", "intellectually curious", "enjoy collaborating", etc. Thus Skills section:- Passionate about cutting‑edge technology and keen on applying new AI/ML algorithms and approaches
- Analytically driven and intellectually curious
- Experienced leading the development and implementation of data and analytic solutions to solve challenging business problems
- Enjoy collaborating with other data scientists to crack hard‑to‑solve problems with AI/ML and seeing it deployed in‑market
- Enjoy collaborating with a multi‑disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders
About the role
Senior Data Scientist (Generative AI), Data Science Lab Direct Hire Hybrid. Office locations: NYC, NY; Holmdel, NJ; Stamford, CT; Bethlehem, PA; Boston, MA. We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads on high-impact high-visibility projects to deliver AI