Principal Data Scientist - AI for Data and Assessment

We need to output HTML fragment with sections: About the role? The posting includes description: "Data is at the center..." then "Team Description". Then responsibilities bullet list: "In this role, you will:" with bullet points. Then "The Ideal Candidate is:" with bullet points. Then Basic Qualifications, Preferred Qualifications. Also salary info: location-specific ranges. Also benefits mention: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits". Also mention performance based incentive compensation. Also mention "This role is expected to accept applications for a minimum of 5 business days." That's application instruction? Should be removed? The instruction says remove application instructions. That line about accepting applications maybe considered application instruction? It's not exactly instruction to apply but could be considered meta. Safer to remove. Also remove EEO, legal boilerplate, accommodation, etc. Also remove "No agencies please." Also remove "Capital One is an equal opportunity employer..." etc. Also remove "Capital One promotes a drug-free workplace." etc. Also remove "Capital One will consider for employment qualified applicants with a criminal history..." etc. Also remove accommodation contact lines. Also remove "For technical support..." etc. Also remove "Capital One does not provide, endorse nor guarantee..." etc. Also remove "Capital One Financial is made up..." etc. We need to include sections only when there is real specific content. So we can have sections: About the role (maybe combine initial paragraph and team description). Responsibilities (the bullet list). Requirements (maybe combine Ideal Candidate bullet list? Actually Ideal Candidate is more like qualifications/skills. Could be Qualifications or Skills. We have Basic Qualifications and Preferred Qualifications separate. Could combine into Requirements section with sub-sections? But spec says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can choose which to include. We have content for About the role (intro + team description). Responsibilities (the bullet list). Qualifications (Basic and Preferred). Skills maybe from Ideal Candidate bullet list (Innovative, Creative, Technical, Statistically-minded, Data guru). Benefits (mention of comprehensive benefits and incentive compensation). Pay (salary ranges). Schedule not mentioned, so omit. We must not repeat job title as heading. No Job ID. We must use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve ALL substantive information; do not invent or summarize away detail. So we need to keep all bullet points exactly as given. Let's extract content. Intro paragraph: "Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives." Team Description: "The AI for Data team builds and ships state of the art machine learning solutions to support data lifecycle within the enterprise. We partner with product, tech and design teams to deliver personalized experiences to our data and platform users to drive productivity and innovation. You will be the driving force to experiment, innovate and create next generation experiences powered by the latest emerging ML technologies." We can combine these into About the role section. Responsibilities bullet list (under "In this role, you will:"): - Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love - Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data - Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation - Flex your interpersonal skills to translate the complexity of your work into tangible business goals Ideal Candidate bullet list (under "The Ideal Candidate is:"): - Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. - Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea. - Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. - Statistically-minded. You’ve built models, validated them, and back tested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning. - A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. Basic Qualifications: list three options: - Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: - A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics - A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics - A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) Preferred Qualifications: - Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) - At least 1 year of experience working with AWS - At least 3 years’ experience in Python, SQL - At least 3 years’ experience with Deep Learning Frameworks - At least 3 years’ experience with Natural Language Processing Also note: "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." That's substantive; should be included maybe under About the role or a separate note. Could be placed in About the role paragraph? Might be better as a separate short paragraph under About the role? But spec says we can have prose

  . We'll include it as a sentence in About the role maybe after team description. Benefits: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level." Also mention performance based incentive compensation: "This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan." We should keep that. Pay: location salary ranges: - McLean, VA: $161,800 - $184,600 for Princ Associate, Data Science - New York, NY: $176,500 - $201,400 for Princ Associate, Data Science - San Jose, CA: $176,500 - $201,400 for Princ Associate, Data Science - Note about other locations: "Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter." We should keep that. Schedule: none. We must not include boilerplate like EEO, accommodation, legal, etc. Now construct HTML. Structure:

  About the role

  Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. The AI for Data team builds and ships state of the art machine learning solutions to support data lifecycle within the enterprise. We partner with product, tech and design teams to deliver personalized experiences to our data and platform users to drive productivity and innovation. You will be the driving force to experiment, innovate and create next generation experiences powered by the latest emerging ML technologies. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

  Responsibilities

  • Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  • Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals

  Qualifications

  Maybe split into Basic and Preferred? Could have two subheadings? But spec only allows those headings; we could use

  Basic Qualifications

  and

  Preferred Qualifications

  as they are not in the list but maybe allowed? The instruction says use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive. Safer to stick to those examples. Could combine Basic and Preferred into a single Qualifications section with prose? But we need to preserve all detail; we can keep bullet lists under Qualifications. Could separate Basic and Preferred as

  ? Not allowed? The instruction didn't forbid other headings, but said use

  for section headings (e.g. ...). It might be okay to use other headings but better to follow examples. We could use

  Qualifications

  and then inside have

  Basic Qualifications:

  then
  ... then

  Preferred Qualifications:

  then
  ... That preserves detail. Similarly Skills could be from Ideal Candidate bullet list; we could put under

  Skills

  with bullet list. Benefits:

  Benefits

  with
  maybe? Benefits are prose; we can put

  Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

  plus another

  This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.